Go-Explore achieved breakthrough performance on challenging reinforcement learning (RL) tasks with sparse rewards. The key insight of Go-Explore was that successful exploration requires an agent to first return to an interesting state ('Go'), and only then explore into unknown terrain ('Explore'). We refer to such exploration after a goal is reached as 'post-exploration'. In this paper we present a systematic study of post-exploration, answering open questions that the Go-Explore paper did not answer yet. First, we study the isolated potential of post-exploration, by turning it on and off within the same algorithm. Subsequently, we introduce new methodology to adaptively decide when to post-explore and for how long to post-explore. Experiments on a range of MiniGrid environments show that post-exploration indeed boosts performance (with a bigger impact than tuning regular exploration parameters), and this effect is further enhanced by adaptively deciding when and for how long to post-explore. In short, our work identifies adaptive post-exploration as a promising direction for RL exploration research.

Estimating information-theoretic quantities such as entropy and mutual information is central to many problems in statistics and machine learning, but challenging in high dimensions. This paper presents estimators of entropy via inference (EEVI), which deliver upper and lower bounds on many information quantities for arbitrary variables in a probabilistic generative model. These estimators use importance sampling with proposal distribution families that include amortized variational inference and sequential Monte Carlo, which can be tailored to the target model and used to squeeze true information values with high accuracy. We present several theoretical properties of EEVI and demonstrate scalability and efficacy on two problems from the medical domain: (i) in an expert system for diagnosing liver disorders, we rank medical tests according to how informative they are about latent diseases, given a pattern of observed symptoms and patient attributes; and (ii) in a differential equation model of carbohydrate metabolism, we find optimal times to take blood glucose measurements that maximize information about a diabetic patient's insulin sensitivity, given their meal and medication schedule.

In recent years, convolutional neural networks (CNNs) have experienced an increasing interest in their ability to perform a fast approximation of effective hydrodynamic parameters in porous media research and applications. This paper presents a novel methodology for permeability prediction from micro-CT scans of geological rock samples. The training data set for CNNs dedicated to permeability prediction consists of permeability labels that are typically generated by classical lattice Boltzmann methods (LBM) that simulate the flow through the pore space of the segmented image data. We instead perform direct numerical simulation (DNS) by solving the stationary Stokes equation in an efficient and distributed-parallel manner. As such, we circumvent the convergence issues of LBM that frequently are observed on complex pore geometries, and therefore, improve the generality and accuracy of our training data set. Using the DNS-computed permeabilities, a physics-informed CNN PhyCNN) is trained by additionally providing a tailored characteristic quantity of the pore space. More precisely, by exploiting the connection to flow problems on a graph representation of the pore space, additional information about confined structures is provided to the network in terms of the maximum flow value, which is the key innovative component of our workflow. The robustness of this approach is reflected by very high prediction accuracy, which is observed for a variety of sandstone samples from archetypal rock formations.

Machine Learning (ML) algorithms are increasingly used as surrogate models to increase the efficiency of stochastic reliability analyses in geotechnical engineering. This paper presents a highly efficient ML aided reliability technique that is able to accurately predict the results of a Monte Carlo (MC) reliability study, and yet performs 500 times faster. A complete MC reliability analysis on anisotropic heterogeneous slopes consisting of 120,000 simulated samples is conducted in parallel to the proposed ML aided stochastic technique. Comparing the results of the complete MC study and the proposed ML aided technique, the expected errors of the proposed method are realistically examined. Circumventing the time-consuming computation of factors of safety for the training datasets, the proposed technique is more efficient than previous methods. Different ML models, including Random Forest (RF), Support Vector Machine (SVM) and Artificial Neural Networks (ANN) are presented, optimised and compared. The effects of the size and type of training and testing datasets are discussed. The expected errors of the ML predicted probability of failure are characterised by different levels of soil heterogeneity and anisotropy. Using only 1% of MC samples to train ML surrogate models, the proposed technique can accurately predict the probability of failure with mean errors limited to 0.7%. The proposed technique reduces the computational time required for our study from 306 days to only 14 hours, providing 500 times higher efficiency.

Self-supervised protein language models have proved their effectiveness in learning the proteins representations. With the increasing computational power, current protein language models pre-trained with millions of diverse sequences can advance the parameter scale from million-level to billion-level and achieve remarkable improvement. However, those prevailing approaches rarely consider incorporating knowledge graphs (KGs), which can provide rich structured knowledge facts for better protein representations. We argue that informative biology knowledge in KGs can enhance protein representation with external knowledge. In this work, we propose OntoProtein, the first general framework that makes use of structure in GO (Gene Ontology) into protein pre-training models. We construct a novel large-scale knowledge graph that consists of GO and its related proteins, and gene annotation texts or protein sequences describe all nodes in the graph. We propose novel contrastive learning with knowledge-aware negative sampling to jointly optimize the knowledge graph and protein embedding during pre-training. Experimental results show that OntoProtein can surpass state-of-the-art methods with pre-trained protein language models in TAPE benchmark and yield better performance compared with baselines in protein-protein interaction and protein function prediction. Code and datasets are available in https://github.com/zjunlp/OntoProtein.

Deep Learning has revolutionized various fields, including Computer Vision, Natural Language Processing, as well as Biomedical research. Within the field of neuroscience, specifically in electrophysiological neuroimaging, researchers are starting to explore leveraging deep learning to make predictions on their data without extensive feature engineering. The availability of large-scale datasets is a crucial aspect of allowing the experimentation of Deep Learning models. We are publishing the first large-scale clinical EEG dataset that simplifies data access and management for Deep Learning. This dataset contains eyes-closed EEG data prepared from a collection of 1,574 juvenile participants from the Healthy Brain Network. We demonstrate a use case integrating this framework, and discuss why providing such neuroinformatics infrastructure to the community is critical for future scientific discoveries.

In the era of big data, intellectual property-oriented scientific and technological resources show the trend of large data scale, high information density and low value density, which brings severe challenges to the effective use of intellectual property resources, and the demand for mining hidden information in intellectual property is increasing. This makes intellectual property-oriented science and technology resource portraits and analysis of evolution become the current research hotspot. This paper sorts out the construction method of intellectual property resource intellectual portrait and its pre-work property entity extraction and entity completion from the aspects of algorithm classification and general process, and directions for improvement of future methods.

Goal-conditioned Hierarchical Reinforcement Learning (HRL) is a promising approach for scaling up reinforcement learning (RL) techniques. However, it often suffers from training inefficiency as the action space of the high-level, i.e., the goal space, is large. Searching in a large goal space poses difficulty for both high-level subgoal generation and low-level policy learning. In this paper, we show that this problem can be effectively alleviated by restricting the high-level action space from the whole goal space to a $k$-step adjacent region of the current state using an adjacency constraint. We theoretically prove that in a deterministic Markov Decision Process (MDP), the proposed adjacency constraint preserves the optimal hierarchical policy, while in a stochastic MDP the adjacency constraint induces a bounded state-value suboptimality determined by the MDP's transition structure. We further show that this constraint can be practically implemented by training an adjacency network that can discriminate between adjacent and non-adjacent subgoals. Experimental results on discrete and continuous control tasks including challenging simulated robot locomotion and manipulation tasks show that incorporating the adjacency constraint significantly boosts the performance of state-of-the-art goal-conditioned HRL approaches.

In this paper, we present a system to train driving policies from experiences collected not just from the ego-vehicle, but all vehicles that it observes. This system uses the behaviors of other agents to create more diverse driving scenarios without collecting additional data. The main difficulty in learning from other vehicles is that there is no sensor information. We use a set of supervisory tasks to learn an intermediate representation that is invariant to the viewpoint of the controlling vehicle. This not only provides a richer signal at training time but also allows more complex reasoning during inference. Learning how all vehicles drive helps predict their behavior at test time and can avoid collisions. We evaluate this system in closed-loop driving simulations. Our system outperforms all prior methods on the public CARLA Leaderboard by a wide margin, improving driving score by 25 and route completion rate by 24 points. Our method won the 2021 CARLA Autonomous Driving challenge. Code and data are available at https://github.com/dotchen/LAV.

Given a trained model and a data sample, membership-inference (MI) attacks predict whether the sample was in the model's training set. A common countermeasure against MI attacks is to utilize differential privacy (DP) during model training to mask the presence of individual examples. While this use of DP is a principled approach to limit the efficacy of MI attacks, there is a gap between the bounds provided by DP and the empirical performance of MI attacks. In this paper, we derive bounds for the \textit{advantage} of an adversary mounting a MI attack, and demonstrate tightness for the widely-used Gaussian mechanism. We further show bounds on the \textit{confidence} of MI attacks. Our bounds are much stronger than those obtained by DP analysis. For example, analyzing a setting of DP-SGD with $\epsilon=4$ would obtain an upper bound on the advantage of $\approx0.36$ based on our analyses, while getting bound of $\approx 0.97$ using the analysis of previous work that convert $\epsilon$ to membership inference bounds. Finally, using our analysis, we provide MI metrics for models trained on CIFAR10 dataset. To the best of our knowledge, our analysis provides the state-of-the-art membership inference bounds for the privacy.

We present a comparison study between a cluster and factor graph representation of LDPC codes. In probabilistic graphical models, cluster graphs retain useful dependence between random variables during inference, which are advantageous in terms of computational cost, convergence speed, and accuracy of marginal probabilities. This study investigates these benefits in the context of LDPC codes and shows that a cluster graph representation outperforms the traditional factor graph representation.

The goal of this work is to propose a robust, fast, and fully automatic method for personalized cranial defect reconstruction and implant modeling. We propose a two-step deep learning-based method using a modified U-Net architecture to perform the defect reconstruction, and a dedicated iterative procedure to improve the implant geometry, followed by automatic generation of models ready for 3-D printing. We propose a cross-case augmentation based on imperfect image registration combining cases from different datasets. We perform ablation studies regarding different augmentation strategies and compare them to other state-of-the-art methods. We evaluate the method on three datasets introduced during the AutoImplant 2021 challenge, organized jointly with the MICCAI conference. We perform the quantitative evaluation using the Dice and boundary Dice coefficients, and the Hausdorff distance. The average Dice coefficient, boundary Dice coefficient, and the 95th percentile of Hausdorff distance are 0.91, 0.94, and 1.53 mm respectively. We perform an additional qualitative evaluation by 3-D printing and visualization in mixed reality to confirm the implant's usefulness. We propose a complete pipeline that enables one to create the cranial implant model ready for 3-D printing. The described method is a greatly extended version of the method that scored 1st place in all AutoImplant 2021 challenge tasks. We freely release the source code, that together with the open datasets, makes the results fully reproducible. The automatic reconstruction of cranial defects may enable manufacturing personalized implants in a significantly shorter time, possibly allowing one to perform the 3-D printing process directly during a given intervention. Moreover, we show the usability of the defect reconstruction in mixed reality that may further reduce the surgery time.

As machine learning models are deployed ever more broadly, it becomes increasingly important that they are not only able to perform well on their training distribution, but also yield accurate predictions when confronted with distribution shift. The Distributionally Robust Optimization (DRO) framework proposes to address this issue by training models to minimize their expected risk under a collection of distributions, to imitate test-time shifts. This is most commonly achieved by instance-level re-weighting of the training objective to emulate the likelihood ratio with possible test distributions, which allows for estimating their empirical risk via importance sampling (assuming that they are subpopulations of the training distribution). However, re-weighting schemes in the literature are usually limited due to the difficulty of keeping the optimization problem tractable and the complexity of enforcing normalization constraints. In this paper, we show that three simple ideas -- mini-batch level normalization, a KL penalty and simultaneous gradient updates -- allow us to train models with DRO using a broader class of parametric likelihood ratios. In a series of experiments on both image and text classification benchmarks, we find that models trained with the resulting parametric adversaries are consistently more robust to subpopulation shifts when compared to other DRO approaches, and that the method performs reliably well with little hyper-parameter tuning. Code to reproduce our experiments can be found at https://github.com/pmichel31415/P-DRO.

We present a method that uses high-resolution topography data of rough terrain, and ground vehicle simulation, to predict traversability. Traversability is expressed as three independent measures: the ability to traverse the terrain at a target speed, energy consumption, and acceleration. The measures are continuous and reflect different objectives for planning that go beyond binary classification. A deep neural network is trained to predict the traversability measures from the local heightmap and target speed. To produce training data, we use an articulated vehicle with wheeled bogie suspensions and procedurally generated terrains. We evaluate the model on laser-scanned forest terrains, previously unseen by the model. The model predicts traversability with an accuracy of 90%. Predictions rely on features from the high-dimensional terrain data that surpass local roughness and slope relative to the heading. Correlations show that the three traversability measures are complementary to each other. With an inference speed 3000 times faster than the ground truth simulation and trivially parallelizable, the model is well suited for traversability analysis and optimal path planning over large areas.

Since 2016, we have witnessed the tremendous growth of artificial intelligence+visualization (AI+VIS) research. However, existing survey papers on AI+VIS focus on visual analytics and information visualization, not scientific visualization (SciVis). In this paper, we survey related deep learning (DL) works in SciVis, specifically in the direction of DL4SciVis: designing DL solutions for solving SciVis problems. To stay focused, we primarily consider works that handle scalar and vector field data but exclude mesh data. We classify and discuss these works along six dimensions: domain setting, research task, learning type, network architecture, loss function, and evaluation metric. The paper concludes with a discussion of the remaining gaps to fill along the discussed dimensions and the grand challenges we need to tackle as a community. This state-of-the-art survey guides SciVis researchers in gaining an overview of this emerging topic and points out future directions to grow this research.

Modern large language models require distributed training strategies due to their size. The challenges of efficiently and robustly training them are met with rapid developments on both software and hardware frontiers. In this technical report, we explore challenges and design decisions associated with developing a scalable training framework, and present a quantitative analysis of efficiency improvements coming from adopting new software and hardware solutions.

Sentiment Analysis is a vital research topic in the field of Computer Science. With the accelerated development of Information Technology and social networks, a massive amount of data related to comment texts has been generated on web applications or social media platforms like Twitter. Due to this, people have actively started proliferating general information and the information related to political opinions, which becomes an important reason for analyzing public reactions. Most researchers have used social media specifics or contents to analyze and predict public opinion concerning political events. This research proposes an analytical study using Israeli political Twitter data to interpret public opinion towards the Palestinian-Israeli conflict. The attitudes of ethnic groups and opinion leaders in the form of tweets are analyzed using Machine Learning algorithms like Support Vector Classifier (SVC), Decision Tree (DT), and Naive Bayes (NB). Finally, a comparative analysis is done based on experimental results from different models.

The demand of probabilistic time series forecasting has been recently raised in various dynamic system scenarios, for example, system identification and prognostic and health management of machines. To this end, we combine the advances in both deep generative models and state space model (SSM) to come up with a novel, data-driven deep probabilistic sequence model. Specifically, we follow the popular encoder-decoder generative structure to build the recurrent neural networks (RNN) assisted variational sequence model on an augmented recurrent input space, which could induce rich stochastic sequence dependency. Besides, in order to alleviate the inconsistency issue of the posterior between training and predicting as well as improving the mining of dynamic patterns, we (i) propose using a lagged hybrid output as input for the posterior at next time step, which brings training and predicting into alignment; and (ii) further devise a generalized auto-regressive strategy that encodes all the historical dependencies for the posterior. Thereafter, we first investigate the methodological characteristics of the proposed deep probabilistic sequence model on toy cases, and then comprehensively demonstrate the superiority of our model against existing deep probabilistic SSM models through extensive numerical experiments on eight system identification benchmarks from various dynamic systems. Finally, we apply our sequence model to a real-world centrifugal compressor forecasting problem, and again verify its outstanding performance by quantifying the time series predictive distribution.

Constrained optimization problems can be difficult because their search spaces have properties not conducive to search, e.g., multimodality, discontinuities, or deception. To address such difficulties, considerable research has been performed on creating novel evolutionary algorithms or specialized genetic operators. However, if the representation that defined the search space could be altered such that it only permitted valid solutions that satisfied the constraints, the task of finding the optimal would be made more feasible without any need for specialized optimization algorithms. We propose Constrained Optimization in Latent Space (COIL), which uses a VAE to generate a learned latent representation from a dataset comprising samples from the valid region of the search space according to a constraint, thus enabling the optimizer to find the objective in the new space defined by the learned representation. Preliminary experiments show promise: compared to an identical GA using a standard representation that cannot meet the constraints or find fit solutions, COIL with its learned latent representation can perfectly satisfy different types of constraints while finding high-fitness solutions.

In some scenarios, classifier requires detecting out-of-distribution samples far from its training data. With desirable characteristics, reconstruction autoencoder-based methods deal with this problem by using input reconstruction error as a metric of novelty vs. normality. We formulate the essence of such approach as a quadruplet domain translation with an intrinsic bias to only query for a proxy of conditional data uncertainty. Accordingly, an improvement direction is formalized as maximumly compressing the autoencoder's latent space while ensuring its reconstructive power for acting as a described domain translator. From it, strategies are introduced including semantic reconstruction, data certainty decomposition and normalized L2 distance to substantially improve original methods, which together establish state-of-the-art performance on various benchmarks, e.g., the FPR@95%TPR of CIFAR-100 vs. TinyImagenet-crop on Wide-ResNet is 0.2%. Importantly, our method works without any additional data, hard-to-implement structure, time-consuming pipeline, and even harming the classification accuracy of known classes.

Massive open online courses (MOOCs), which provide a large-scale interactive participation and open access via the web, are becoming a modish way for online and distance education. To help users have a better study experience, many MOOC platforms have provided the services of recommending courses to users. However, we argue that directly recommending a course to users will ignore the expertise levels of different users. To fill this gap, this paper studies the problem of concept recommendation in a more fine-grained view. We propose a novel Heterogeneous Information Networks based Concept Recommender with Reinforcement Learning (HinCRec-RL) incorporated for concept recommendation in MOOCs. Specifically, we first formulate the concept recommendation in MOOCs as a reinforcement learning problem to better model the dynamic interaction among users and knowledge concepts. In addition, to mitigate the data sparsity issue which also exists in many other recommendation tasks, we consider a heterogeneous information network (HIN) among users, courses, videos and concepts, to better learn the semantic representation of users. In particular, we use the meta-paths on HIN to guide the propagation of users' preferences and propose a heterogeneous graph attention network to represent the meta-paths. To validate the effectiveness of our proposed approach, we conduct comprehensive experiments on a real-world dataset from XuetangX, a popular MOOC platform from China. The promising results show that our proposed approach can outperform other baselines.

In this paper, we propose a dynamic cascaded encoder Automatic Speech Recognition (ASR) model, which unifies models for different deployment scenarios. Moreover, the model can significantly reduce model size and power consumption without loss of quality. Namely, with the dynamic cascaded encoder model, we explore three techniques to maximally boost the performance of each model size: 1) Use separate decoders for each sub-model while sharing the encoders; 2) Use funnel-pooling to improve the encoder efficiency; 3) Balance the size of causal and non-causal encoders to improve quality and fit deployment constraints. Overall, the proposed large-medium model has 30% smaller size and reduces power consumption by 33%, compared to the baseline cascaded encoder model. The triple-size model that unifies the large, medium, and small models achieves 37% total size reduction with minimal quality loss, while substantially reducing the engineering efforts of having separate models.

Providing reliable connectivity to cellular-connected UAV can be very challenging; their performance highly depends on the nature of the surrounding environment, such as density and heights of the ground BSs. On the other hand, tall buildings might block undesired interference signals from ground BSs, thereby improving the connectivity between the UAVs and their serving BSs. To address the connectivity of UAVs in such environments, this paper proposes a RL algorithm to dynamically optimise the height of a UAV as it moves through the environment, with the goal of increasing the throughput or spectrum efficiency that it experiences. The proposed solution is evaluated in two settings: using a series of generated environments where we vary the number of BS and building densities, and in a scenario using real-world data obtained from an experiment in Dublin, Ireland. Results show that our proposed RL-based solution improves UAVs QoS by 6% to 41%, depending on the scenario. We also conclude that, when flying at heights higher than the buildings, building density variation has no impact on UAV QoS. On the other hand, BS density can negatively impact UAV QoS, with higher numbers of BSs generating more interference and deteriorating UAV performance.

Automated segmentation of pathological regions of interest aids medical image diagnostics and follow-up care. However, accurate pathological segmentations require high quality of annotated data that can be both cost and time intensive to generate. In this work, we propose an automated two-step method that detects a minimal image subset required to train segmentation models by evaluating the quality of medical images from 3D image stacks using a U-net++ model. These images that represent a lack of quality training can then be annotated and used to fully train a U-net-based segmentation model. The proposed QU-net++ model detects this lack of quality training based on the disagreement in segmentations produced from the final two output layers. The proposed model isolates around 10% of the slices per 3D image stack and can scale across imaging modalities to segment cysts in OCT images and ground glass opacity (GGO) in lung CT images with Dice scores in the range 0.56-0.72. Thus, the proposed method can be applied for cost effective multi-modal pathology segmentation tasks.

Deep learning oriented named entity recognition (DNER) has gradually become the paradigm of knowledge discovery, which greatly promotes domain intelligence. However, the current activation function of DNER fails to treat gradient vanishing, no negative output or non-differentiable existence, which may impede knowledge exploration caused by the omission and incomplete representation of latent semantics. To break through the dilemma, we present a novel activation function termed KDAC. Detailly, KDAC is an aggregation function with multiple conversion modes. The backbone of the activation region is the interaction between exponent and linearity, and the both ends extend through adaptive linear divergence, which surmounts the obstacle of gradient vanishing and no negative output. Crucially, the non-differentiable points are alerted and eliminated by an approximate smoothing algorithm. KDAC has a series of brilliant properties, including nonlinear, stable near-linear transformation and derivative, as well as dynamic style, etc. We perform experiments based on BERT-BiLSTM-CNN-CRF model on six benchmark datasets containing different domain knowledge, such as Weibo, Clinical, E-commerce, Resume, HAZOP and People's daily. The evaluation results show that KDAC is advanced and effective, and can provide more generalized activation to stimulate the performance of DNER. We hope that KDAC can be exploited as a promising activation function to devote itself to the construction of knowledge.

Machine learning models have been widely developed, released, and adopted in numerous applications. Meanwhile, the documentation practice for machine learning models often falls short of established practices for traditional software components, which impedes model accountability, inadvertently abets inappropriate or misuse of models, and may trigger negative social impact. Recently, model cards, a template for documenting machine learning models, have attracted notable attention, but their impact on the practice of model documentation is unclear. In this work, we examine publicly available model cards and other similar documentation. Our analysis reveals a substantial gap between the suggestions made in the original model card work and the content in actual documentation. Motivated by this observation and literature on fields such as software documentation, interaction design, and traceability, we further propose a set of design guidelines that aim to support the documentation practice for machine learning models including (1) the collocation of documentation environment with the coding environment, (2) nudging the consideration of model card sections during model development, and (3) documentation derived from and traced to the source. We designed a prototype tool named DocML following those guidelines to support model development in computational notebooks. A lab study reveals the benefit of our tool to shift the behavior of data scientists towards documentation quality and accountability.

Given a sequence of sets, where each set is associated with a timestamp and contains an arbitrary number of elements, the task of temporal sets prediction aims to predict the elements in the subsequent set. Previous studies for temporal sets prediction mainly capture each user's evolutionary preference by learning from his/her own sequence. Although insightful, we argue that: 1) the collaborative signals latent in different users' sequences are essential but have not been exploited; 2) users also tend to show stationary preferences while existing methods fail to consider. To this end, we propose an integrated learning framework to model both the evolutionary and the stationary preferences of users for temporal sets prediction, which first constructs a universal sequence by chronologically arranging all the user-set interactions, and then learns on each user-set interaction. In particular, for each user-set interaction, we first design an evolutionary user preference modelling component to track the user's time-evolving preference and exploit the latent collaborative signals among different users. This component maintains a memory bank to store memories of the related user and elements, and continuously updates their memories based on the currently encoded messages and the past memories. Then, we devise a stationary user preference modelling module to discover each user's personalized characteristics according to the historical sequence, which adaptively aggregates the previously interacted elements from dual perspectives with the guidance of the user's and elements' embeddings. Finally, we develop a set-batch algorithm to improve the model efficiency, which can create time-consistent batches in advance and achieve 3.5x training speedups on average. Experiments on real-world datasets demonstrate the effectiveness and good interpretability of our approach.

Hypergraphs (i.e., sets of hyperedges) naturally represent group relations (e.g., researchers co-authoring a paper and ingredients used together in a recipe), each of which corresponds to a hyperedge (i.e., a subset of nodes). Predicting future or missing hyperedges bears significant implication for many applications (e.g., collaboration and recipe recommendation). What makes hyperedge prediction particularly challenging is the vast number of non-hyperedge subsets, which grows exponentially with the number of nodes. Since it is prohibitive to use all of them as negative examples for model training, it is inevitable to sample a very small portion of them, and to this end, heuristic sampling schemes have been employed. However, trained models suffer from poor generalization capability for examples of different natures. In this paper, we propose AHP, an adversarial training-based hyperedge-prediction method. It learns to sample negative examples without relying on any heuristic schemes. Using six real hypergraphs, we show that AHP generalizes better to negative examples of various natures. It yields up to 28.2% higher AUROC than best existing methods and often even outperforms its variants with sampling schemes tailored to test sets.

Weak supervision (WS) is an alternative to the traditional supervised learning to address the need for ground truth. Data programming is a practical WS approach that allows programmatic labeling data samples using labeling functions (LFs) instead of hand-labeling each data point. However, the existing approach fails to fully exploit the domain knowledge encoded into LFs, especially when the LFs' coverage is low. This is due to the common data programming pipeline that neglects to utilize data features during the generative process. This paper proposes a new approach called reinforced labeling (RL). Given an unlabeled dataset and a set of LFs, RL augments the LFs' outputs to cases not covered by LFs based on similarities among samples. Thus, RL can lead to higher labeling coverage for training an end classifier. The experiments on several domains (classification of YouTube comments, wine quality, and weather prediction) result in considerable gains. The new approach produces significant performance improvement, leading up to +21 points in accuracy and +61 points in F1 scores compared to the state-of-the-art data programming approach.

The ongoing pandemic has highlighted the importance of reliable and efficient clinical trials in healthcare. Trial sites, where the trials are conducted, are chosen mainly based on feasibility in terms of medical expertise and access to a large group of patients. More recently, the issue of diversity and inclusion in clinical trials is gaining importance. Different patient groups may experience the effects of a medical drug/ treatment differently and hence need to be included in the clinical trials. These groups could be based on ethnicity, co-morbidities, age, or economic factors. Thus, designing a method for trial site selection that accounts for both feasibility and diversity is a crucial and urgent goal. In this paper, we formulate this problem as a ranking problem with fairness constraints. Using principles of fairness in machine learning, we learn a model that maps a clinical trial description to a ranked list of potential trial sites. Unlike existing fairness frameworks, the group membership of each trial site is non-binary: each trial site may have access to patients from multiple groups. We propose fairness criteria based on demographic parity to address such a multi-group membership scenario. We test our method on 480 real-world clinical trials and show that our model results in a list of potential trial sites that provides access to a diverse set of patients while also ensuing a high number of enrolled patients.

Running large-scale pre-trained language models in computationally constrained environments remains a challenging problem yet to be addressed, while transfer learning from these models has become prevalent in Natural Language Processing tasks. Several solutions, including knowledge distillation, network quantization, or network pruning have been previously proposed; however, these approaches focus mostly on the English language, thus widening the gap when considering low-resource languages. In this work, we introduce three light and fast versions of distilled BERT models for the Romanian language: Distil-BERT-base-ro, Distil-RoBERT-base, and DistilMulti-BERT-base-ro. The first two models resulted from the individual distillation of knowledge from two base versions of Romanian BERTs available in literature, while the last one was obtained by distilling their ensemble. To our knowledge, this is the first attempt to create publicly available Romanian distilled BERT models, which were thoroughly evaluated on five tasks: part-of-speech tagging, named entity recognition, sentiment analysis, semantic textual similarity, and dialect identification. Our experimental results argue that the three distilled models offer performance comparable to their teachers, while being twice as fast on a GPU and ~35% smaller. In addition, we further test the similarity between the predictions of our students versus their teachers by measuring their label and probability loyalty, together with regression loyalty - a new metric introduced in this work.

The accuracy of the estimated stellar atmospheric parameter decreases evidently with the decreasing of spectral signal-to-noise ratio (SNR) and there are a huge amount of this kind observations, especially in case of SNR$<$30. Therefore, it is helpful to improve the parameter estimation performance for these spectra and this work studied the ($T_\texttt{eff}, \log~g$, [Fe/H]) estimation problem for LAMOST DR8 low-resolution spectra with 20$\leq$SNR$<$30. We proposed a data-driven method based on machine learning techniques. Firstly, this scheme detected stellar atmospheric parameter-sensitive features from spectra by the Least Absolute Shrinkage and Selection Operator (LASSO), rejected ineffective data components and irrelevant data. Secondly, a Multi-layer Perceptron (MLP) method was used to estimate stellar atmospheric parameters from the LASSO features. Finally, the performance of the LASSO-MLP was evaluated by computing and analyzing the consistency between its estimation and the reference from the APOGEE (Apache Point Observatory Galactic Evolution Experiment) high-resolution spectra. Experiments show that the Mean Absolute Errors (MAE) of $T_\texttt{eff}, \log~g$, [Fe/H] are reduced from the LASP (137.6 K, 0.195 dex, 0.091 dex) to LASSO-MLP (84.32 K, 0.137 dex, 0.063 dex), which indicate evident improvements on stellar atmospheric parameter estimation. In addition, this work estimated the stellar atmospheric parameters for 1,162,760 low-resolution spectra with 20$\leq$SNR$<$30 from LAMOST DR8 using LASSO-MLP, and released the estimation catalog, learned model, experimental code, trained model, training data and test data for scientific exploration and algorithm study.

Future Moon bases will likely be constructed using resources mined from the surface of the Moon. The difficulty of maintaining a human workforce on the Moon and communications lag with Earth means that mining will need to be conducted using collaborative robots with a high degree of autonomy. In this paper, we describe our solution for Phase 2 of the NASA Space Robotics Challenge, which provided a simulated lunar environment in which teams were tasked to develop software systems to achieve autonomous collaborative robots for mining on the Moon. Our 3rd place and innovation award winning solution shows how machine learning-enabled vision could alleviate major challenges posed by the lunar environment towards autonomous space mining, chiefly the lack of satellite positioning systems, hazardous terrain, and delicate robot interactions. A robust multi-robot coordinator was also developed to achieve long-term operation and effective collaboration between robots.

In the Big Data era, with the ubiquity of geolocation sensors in particular, massive datasets exhibiting a possibly complex spatial dependence structure are becoming increasingly available. In this context, the standard probabilistic theory of statistical learning does not apply directly and guarantees of the generalization capacity of predictive rules learned from such data are left to establish. We analyze here the simple Kriging task, the flagship problem in Geostatistics: the values of a square integrable random field $X=\{X_s\}_{s\in S}$, $S\subset \mathbb{R}^2$, with unknown covariance structure are to be predicted with minimum quadratic risk, based upon observing a single realization of the spatial process at a finite number of locations $s_1,\; \ldots,\; s_n$ in $S$. Despite the connection of this minimization problem with kernel ridge regression, establishing the generalization capacity of empirical risk minimizers is far from straightforward, due to the non i.i.d. nature of the spatial data $X_{s_1},\; \ldots,\; X_{s_n}$ involved. In this article, nonasymptotic bounds of order $O_{\mathbb{P}}(1/n)$ are proved for the excess risk of a plug-in predictive rule mimicking the true minimizer in the case of isotropic stationary Gaussian processes observed at locations forming a regular grid. These theoretical results, as well as the role played by the technical conditions required to establish them, are illustrated by various numerical experiments and hopefully pave the way for further developments in statistical learning based on spatial data.

We consider high-dimensional multiclass classification by sparse multinomial logistic regression. Unlike binary classification, in the multiclass setup one can think about an entire spectrum of possible notions of sparsity associated with different structural assumptions on the regression coefficients matrix. We propose a computationally feasible feature selection procedure based on penalized maximum likelihood with convex penalties capturing a specific type of sparsity at hand. In particular, we consider global sparsity, double row-wise sparsity, and low-rank sparsity, and show that with the properly chosen tuning parameters the derived plug-in classifiers attain the minimax generalization error bounds (in terms of misclassification excess risk) within the corresponding classes of multiclass sparse linear classifiers. The developed approach is general and can be adapted to other types of sparsity as well.

Noting the importance of factorizing (or disentangling) the latent space, we propose a novel, non-probabilistic disentangling framework for autoencoders, based on the principles of symmetry transformations in group-theory. To the best of our knowledge, this is the first deterministic model that is aiming to achieve disentanglement based on autoencoders without regularizers. The proposed model is compared to seven state-of-the-art generative models based on autoencoders and evaluated based on five supervised disentanglement metrics. The experimental results show that the proposed model can have better disentanglement when variances of each features are different. We believe that this model leads to a new field for disentanglement learning based on autoencoders without regularizers.

Graph mining tasks arise from many different application domains, ranging from social networks, transportation, E-commerce, etc., which have been receiving great attention from the theoretical and algorithm design communities in recent years, and there has been some pioneering work using the hotly researched reinforcement learning (RL) techniques to address graph data mining tasks. However, these graph mining algorithms and RL models are dispersed in different research areas, which makes it hard to compare different algorithms with each other. In this survey, we provide a comprehensive overview of RL models and graph mining and generalize these algorithms to Graph Reinforcement Learning (GRL) as a unified formulation. We further discuss the applications of GRL methods across various domains and summarize the method description, open-source codes, and benchmark datasets of GRL methods. Finally, we propose possible important directions and challenges to be solved in the future. This is the latest work on a comprehensive survey of GRL literature, and this work provides a global view for researchers as well as a learning resource for researchers outside the domain. In addition, we create an online open-source for both interested researchers who want to enter this rapidly developing domain and experts who would like to compare GRL methods.

We trained a keyword spotting model using federated learning on real user devices and observed significant improvements when the model was deployed for inference on phones. To compensate for data domains that are missing from on-device training caches, we employed joint federated-centralized training. And to learn in the absence of curated labels on-device, we formulated a confidence filtering strategy based on user-feedback signals for federated distillation. These techniques created models that significantly improved quality metrics in offline evaluations and user-experience metrics in live A/B experiments.

Federated learning (FL) is a promising distributed learning technique particularly suitable for wireless learning scenarios since it can accomplish a learning task without raw data transportation so as to preserve data privacy and lower network resource consumption. However, current works on FL over wireless networks do not profoundly study the fundamental performance of FL over wireless networks that suffers from communication outage due to channel impairment and network interference. To accurately exploit the performance of FL over wireless networks, this paper proposes a novel intermittent FL model over a cellular-connected unmanned aerial vehicle (UAV) network, which characterizes communication outage from UAV (clients) to their server and data heterogeneity among the datasets at UAVs. We propose an analytically tractable framework to derive the uplink outage probability and use it to devise a simulation-based approach so as to evaluate the performance of the proposed intermittent FL model. Our findings reveal how the intermittent FL model is impacted by uplink communication outage and UAV deployment. Extensive numerical simulations are provided to show the consistency between the simulated and analytical performances of the proposed intermittent FL model.

The incredible development of federated learning (FL) has benefited various tasks in the domains of computer vision and natural language processing, and the existing frameworks such as TFF and FATE has made the deployment easy in real-world applications. However, federated graph learning (FGL), even though graph data are prevalent, has not been well supported due to its unique characteristics and requirements. The lack of FGL-related framework increases the efforts for accomplishing reproducible research and deploying in real-world applications. Motivated by such strong demand, in this paper, we first discuss the challenges in creating an easy-to-use FGL package and accordingly present our implemented package FederatedScope-GNN (FS-G), which provides (1) a unified view for modularizing and expressing FGL algorithms; (2) comprehensive DataZoo and ModelZoo for out-of-the-box FGL capability; (3) an efficient model auto-tuning component; and (4) off-the-shelf privacy attack and defense abilities. We validate the effectiveness of FS-G by conducting extensive experiments, which simultaneously gains many valuable insights about FGL for the community. Moreover, we employ FS-G to serve the FGL application in real-world E-commerce scenarios, where the attained improvements indicate great potential business benefits. We publicly release FS-G, as submodules of FederatedScope, at https://github.com/alibaba/FederatedScope to promote FGL's research and enable broad applications that would otherwise be infeasible due to the lack of a dedicated package.

We discuss a way to find a well behaved baseline for attribution methods that work by feeding a neural network with a sequence of interpolated inputs between two given inputs. Then, we test it with our novel Riemann-Stieltjes Integrated Gradient-weighted Class Activation Mapping (RSI-Grad-CAM) attribution method.

Many real-world systems are described not only by data from a single source but via multiple data views. For example, in genomic medicine, a patient can be described by data from different molecular layers. This raises the need for multi-view models that are able to disentangle variation within and across data views in an interpretable manner. Latent variable models with structured sparsity are a commonly used tool to address this modeling task but interpretability is cumbersome since it requires a direct inspection and interpretation of each factor via a specialized domain expert. Here, we propose MuVI, a novel approach for domain-informed multi-view latent variable models, facilitating the analysis of multi-view data in an inherently explainable manner. We demonstrate that our model (i) is able to integrate noisy domain expertise in form of feature sets, (ii) is robust to noise in the encoded domain knowledge, (iii) results in identifiable factors and (iv) is able to infer interpretable and biologically meaningful axes of variation in a real-world multi-view dataset of cancer patients.

Synthetic data generation has proven to be a promising solution for addressing data availability issues in various domains. Even more challenging is the generation of synthetic time series data, where one has to preserve temporal dynamics, i.e., the generated time series must respect the original relationships between variables across time. Recently proposed techniques such as generative adversarial networks (GANs) and quantum-GANs lack the ability to attend to the time series specific temporal correlations adequately. We propose using the inherent nature of quantum computers to simulate quantum dynamics as a technique to encode such features. We start by assuming that a given time series can be generated by a quantum process, after which we proceed to learn that quantum process using quantum machine learning. We then use the learned model to generate out-of-sample time series and show that it captures unique and complex features of the learned time series. We also study the class of time series that can be modeled using this technique. Finally, we experimentally demonstrate the proposed algorithm on an 11-qubit trapped-ion quantum machine.

We present a novel artificial cognitive mapping system using generative deep neural networks, called variational autoencoder/generative adversarial network (VAE/GAN), which can map input images to latent vectors and generate temporal sequences internally. The results show that the distance of the predicted image is reflected in the distance of the corresponding latent vector after training. This indicates that the latent space is self-organized to reflect the proximity structure of the dataset and may provide a mechanism through which many aspects of cognition are spatially represented. The present study allows the network to internally generate temporal sequences that are analogous to the hippocampal replay/pre-play ability, where VAE produces only near-accurate replays of past experiences, but by introducing GANs, the generated sequences are coupled with instability and novelty.

Despite recent improvements in abstractive summarization, most current approaches generate summaries that are not factually consistent with the source document, severely restricting their trust and usage in real-world applications. Recent works have shown promising improvements in factuality error identification using text or dependency arc entailments; however, they do not consider the entire semantic graph simultaneously. To this end, we propose FactGraph, a method that decomposes the document and the summary into structured meaning representations (MR), which are more suitable for factuality evaluation. MRs describe core semantic concepts and their relations, aggregating the main content in both document and summary in a canonical form, and reducing data sparsity. FactGraph encodes such graphs using a graph encoder augmented with structure-aware adapters to capture interactions among the concepts based on the graph connectivity, along with text representations using an adapter-based text encoder. Experiments on different benchmarks for evaluating factuality show that FactGraph outperforms previous approaches by up to 15%. Furthermore, FactGraph improves performance on identifying content verifiability errors and better captures subsentence-level factual inconsistencies.

Neuroscience and neurotechnology are currently being revolutionized by artificial intelligence (AI) and machine learning. AI is widely used to study and interpret neural signals (analytical applications), assist people with disabilities (prosthetic applications), and treat underlying neurological symptoms (therapeutic applications). In this brief, we will review the emerging opportunities of on-chip AI for the next-generation implantable brain-machine interfaces (BMIs), with a focus on state-of-the-art prosthetic BMIs. Major technological challenges for the effectiveness of AI models will be discussed. Finally, we will present algorithmic and IC design solutions to enable a new generation of AI-enhanced and high-channel-count BMIs.

For more than a half-century, credit risk management has used credit scoring models in each of its well-defined stages to manage credit risk. Application scoring is used to decide whether to grant a credit or not, while behavioral scoring is used mainly for portfolio management and to take preventive actions in case of default signals. In both cases, network data has recently been shown to be valuable to increase the predictive power of these models, especially when the borrower's historical data is scarce or not available. This study aims to understand the creditworthiness assessment performance dynamics and how it is influenced by the credit history, repayment behavior, and social network features. To accomplish this, we introduced a machine learning classification framework to analyze 97.000 individuals and companies from the moment they obtained their first loan to 12 months afterward. Our novel and massive dataset allow us to characterize each borrower according to their credit behavior, and social and economic relationships. Our research shows that borrowers' history increases performance at a decreasing rate during the first six months and then stabilizes. The most notable effect on perfomance of social networks features occurs at loan application; in personal scoring, this effect prevails a few months, while in business scoring adds value throughout the study period. These findings are of great value to improve credit risk management and optimize the use of traditional information and alternative data sources.

Speech dereverberation is often an important requirement in robust speech processing tasks. Supervised deep learning (DL) models give state-of-the-art performance for single-channel speech dereverberation. Temporal convolutional networks (TCNs) are commonly used for sequence modelling in speech enhancement tasks. A feature of TCNs is that they have a receptive field (RF) dependant on the specific model configuration which determines the number of input frames that can be observed to produce an individual output frame. It has been shown that TCNs are capable of performing dereverberation of simulated speech data, however a thorough analysis, especially with focus on the RF is yet lacking in the literature. This paper analyses dereverberation performance depending on the model size and the RF of TCNs. Experiments using the WHAMR corpus which is extended to include room impulse responses (RIRs) with larger T60 values demonstrate that a larger RF can have significant improvement in performance when training smaller TCN models. It is also demonstrated that TCNs benefit from a wider RF when dereverberating RIRs with larger RT60 values.

Reinforcement learning algorithms are typically limited to learning a single solution for a specified task, even though diverse solutions often exist. Recent studies showed that learning a set of diverse solutions is beneficial because diversity enables robust few-shot adaptation. Although existing methods learn diverse solutions by using the mutual information as unsupervised rewards, such an approach often suffers from the bias of the gradient estimator induced by value function approximation. In this study, we propose a novel method that can learn diverse solutions without suffering the bias problem. In our method, a policy conditioned on a continuous or discrete latent variable is trained by directly maximizing the variational lower bound of the mutual information, instead of using the mutual information as unsupervised rewards as in previous studies. Through extensive experiments on robot locomotion tasks, we demonstrate that the proposed method successfully learns an infinite set of diverse solutions by learning continuous latent variables, which is more challenging than learning a finite number of solutions. Subsequently, we show that our method enables more effective few-shot adaptation compared with existing methods.

Text-based communication is highly favoured as a communication method, especially in business environments. As a result, it is often abused by sending malicious messages, e.g., spam emails, to deceive users into relaying personal information, including online accounts credentials or banking details. For this reason, many machine learning methods for text classification have been proposed and incorporated into the services of most email providers. However, optimising text classification algorithms and finding the right tradeoff on their aggressiveness is still a major research problem. We present an updated survey of 12 machine learning text classifiers applied to a public spam corpus. A new pipeline is proposed to optimise hyperparameter selection and improve the models' performance by applying specific methods (based on natural language processing) in the preprocessing stage. Our study aims to provide a new methodology to investigate and optimise the effect of different feature sizes and hyperparameters in machine learning classifiers that are widely used in text classification problems. The classifiers are tested and evaluated on different metrics including F-score (accuracy), precision, recall, and run time. By analysing all these aspects, we show how the proposed pipeline can be used to achieve a good accuracy towards spam filtering on the Enron dataset, a widely used public email corpus. Statistical tests and explainability techniques are applied to provide a robust analysis of the proposed pipeline and interpret the classification outcomes of the 12 machine learning models, also identifying words that drive the classification results. Our analysis shows that it is possible to identify an effective machine learning model to classify the Enron dataset with an F-score of 94%.

To analyse local robustness properties of deep neural networks (DNNs), we present a practical framework from a model learning perspective. Based on black-box model learning with scenario optimisation, we abstract the local behaviour of a DNN via an affine model with the probably approximately correct (PAC) guarantee. From the learned model, we can infer the corresponding PAC-model robustness property. The innovation of our work is the integration of model learning into PAC robustness analysis: that is, we construct a PAC guarantee on the model level instead of sample distribution, which induces a more faithful and accurate robustness evaluation. This is in contrast to existing statistical methods without model learning. We implement our method in a prototypical tool named DeepPAC. As a black-box method, DeepPAC is scalable and efficient, especially when DNNs have complex structures or high-dimensional inputs. We extensively evaluate DeepPAC, with 4 baselines (using formal verification, statistical methods, testing and adversarial attack) and 20 DNN models across 3 datasets, including MNIST, CIFAR-10, and ImageNet. It is shown that DeepPAC outperforms the state-of-the-art statistical method PROVERO, and it achieves more practical robustness analysis than the formal verification tool ERAN. Also, its results are consistent with existing DNN testing work like DeepGini.

Spiking neural networks (SNNs) have become an interesting alternative to conventional artificial neural networks (ANN) thanks to their temporal processing capabilities and energy efficient implementations in neuromorphic hardware. However the challenges involved in training SNNs have limited their performance in terms of accuracy and thus their applications. Improving learning algorithms and neural architectures for a more accurate feature extraction is therefore one of the current priorities in SNN research. In this paper we present a study on the key components of modern spiking architectures. We empirically compare different techniques in image classification datasets taken from the best performing networks. We design a spiking version of the successful residual network architecture and provide an in-depth study on the possible implementations of spiking residual connections. Our results provide a state of the art guide to SNN design, which allows to make informed choices when trying to build the optimal visual feature extractor. Finally, our network outperforms previous SNN architectures in CIFAR-10 (94.14%) and CIFAR-100 (74.65%) datasets and matches the state of the art in DVS-CIFAR10 (72.98%), with less parameters than the previous state of the art and without the need for ANN-SNN conversion. Code available at https://github.com/VicenteAlex/Spiking_ResNet

Criminals have become increasingly experienced in using cryptocurrencies, such as Bitcoin, for money laundering. The use of cryptocurrencies can hide criminal identities and transfer hundreds of millions of dollars of dirty funds through their criminal digital wallets. However, this is considered a paradox because cryptocurrencies are gold mines for open-source intelligence, allowing law enforcement agencies to have more power in conducting forensic analyses. This paper proposed Inspection-L, a graph neural network (GNN) framework based on self-supervised Deep Graph Infomax (DGI), with supervised learning algorithms, namely Random Forest (RF) to detect illicit transactions for AML. To the best of our knowledge, our proposal is the first of applying self-supervised GNNs to the problem of AML in Bitcoin. The proposed method has been evaluated on the Elliptic dataset and shows that our approach outperforms the baseline in terms of key classification metrics, which demonstrates the potential of self-supervised GNN in cryptocurrency illicit transaction detection.

Machine learning (ML) is widely used in drug discovery to train models that predict protein-ligand binding. These models are of great value to medicinal chemists, in particular if they provide case-specific insight into the physical interactions that drive the binding process. In this study we derive ML models from over 50 fragment-screening campaigns to introduce two important elements that we believe are absent in most -- if not all -- ML studies of this type reported to date: First, alongside the observed hits we use to train our models, we incorporate true misses and show that these experimentally validated negative data are of significant importance to the quality of the derived models. Second, we provide a physically interpretable and verifiable representation of what the ML model considers important for successful binding. This representation is derived from a straightforward attribution procedure that explains the prediction in terms of the (inter-)action of chemical environments. Critically, we validate the attribution outcome on a large scale against prior annotations made independently by expert molecular modellers. We find good agreement between the key molecular substructures proposed by the ML model and those assigned manually, even when the model's performance in discriminating hits from misses is far from perfect. By projecting the attribution onto predefined interaction prototypes (pharmacophores), we show that ML allows us to formulate simple rules for what drives fragment binding against a target automatically from screening data.

This work addresses the problem of exploration in an unknown environment. For linear dynamical systems, we use an experimental design framework and introduce an online greedy policy where the control maximizes the information of the next step. In a setting with a limited number of experimental trials, our algorithm has low complexity and shows experimentally competitive performances compared to more elaborate gradient-based methods.

Min-max optimization problems (i.e., min-max games) have been attracting a great deal of attention because of their applicability to a wide range of machine learning problems. Although significant progress has been made recently, the literature to date has focused on games with independent strategy sets; little is known about solving games with dependent strategy sets, which can be characterized as min-max Stackelberg games. We introduce two first-order methods that solve a large class of convex-concave min-max Stackelberg games, and show that our methods converge in polynomial time. Min-max Stackelberg games were first studied by Wald, under the posthumous name of Wald's maximin model, a variant of which is the main paradigm used in robust optimization, which means that our methods can likewise solve many convex robust optimization problems. We observe that the computation of competitive equilibria in Fisher markets also comprises a min-max Stackelberg game. Further, we demonstrate the efficacy and efficiency of our algorithms in practice by computing competitive equilibria in Fisher markets with varying utility structures. Our experiments suggest potential ways to extend our theoretical results, by demonstrating how different smoothness properties can affect the convergence rate of our algorithms.

In this paper we present a hybrid neural network augmented physics-based modeling (APBM) framework for Bayesian nonlinear latent space estimation. The proposed APBM strategy allows for model adaptation when new operation conditions come into play or the physics-based model is insufficient (or incomplete) to properly describe the latent phenomenon. One advantage of the APBMs and our estimation procedure is the capability of maintaining the physical interpretability of estimated states. Furthermore, we propose a constraint filtering approach to control the neural network contributions to the overall model. We also exploit assumed density filtering techniques and cubature integration rules to present a flexible estimation strategy that can easily deal with nonlinear models and high-dimensional latent spaces. Finally, we demonstrate the efficacy of our methodology by leveraging a target tracking scenario with nonlinear and incomplete measurement and acceleration models, respectively.

As machine learning models start to be used in critical applications, their vulnerabilities and brittleness become a pressing concern. Adversarial attacks are a popular framework for studying these vulnerabilities. In this work, we study the error of linear regression in the face of adversarial attacks. We provide bounds of the error in terms of the traditional risk and the parameter norm and show how these bounds can be leveraged and make it possible to use analysis from non-adversarial setups to study the adversarial risk. The usefulness of these results is illustrated by shedding light on whether or not overparameterized linear models can be adversarially robust. We show that adding features to linear models might be either a source of additional robustness or brittleness. We show that these differences appear due to scaling and how the $\ell_1$ and $\ell_2$ norms of random projections concentrate. We also show how the reformulation we propose allows for solving adversarial training as a convex optimization problem. This is then used as a tool to study how adversarial training and other regularization methods might affect the robustness of the estimated models.

When learning to act in a stochastic, partially observable environment, an intelligent agent should be prepared to anticipate a change in its belief of the environment state, and be capable of adapting its actions on-the-fly to changing conditions. As humans, we are able to form contingency plans when learning a task with the explicit aim of being able to correct errors in the initial control, and hence prove useful if ever there is a sudden change in our perception of the environment which requires immediate corrective action. This is especially the case for autonomous vehicles (AVs) navigating real-world situations where safety is paramount, and a strong ability to react to a changing belief about the environment is truly needed. In this paper we explore an end-to-end approach, from training to execution, for learning robust contingency plans and combining them with a hierarchical planner to obtain a robust agent policy in an autonomous navigation task where other vehicles' behaviours are unknown, and the agent's belief about these behaviours is subject to sudden, last-second change. We show that our approach results in robust, safe behaviour in a partially observable, stochastic environment, generalizing well over environment dynamics not seen during training.

Beam codebooks are a new feature of massive multiple-input multiple-output (M-MIMO) in 5G new radio (NR). Codebooks comprised of beamforming vectors are used to transmit reference signals and obtain limited channel state information (CSI) from receivers via the codeword index. This enables large arrays that cannot otherwise obtain sufficient CSI. The performance, however, is limited by the codebook design. In this paper, we show that machine learning can be used to train site-specific codebooks for initial access. We design a neural network based on an autoencoder architecture that uses a beamspace observation in combination with RF environment characteristics to improve the synchronization signal (SS) burst codebook. We test our algorithm using a flexible dataset of channels generated from QuaDRiGa. The results show that our model outperforms the industry standard (DFT beams) and approaches the optimal performance (perfect CSI and singular value decomposition (SVD)-based beamforming), using only a few bits of feedback.

Decentralized learning provides an effective framework to train machine learning models with data distributed over arbitrary communication graphs. However, most existing approaches toward decentralized learning disregard the interaction between data heterogeneity and graph topology. In this paper, we characterize the dependence of convergence on the relationship between the mixing weights of the graph and the data heterogeneity across nodes. We propose a metric that quantifies the ability of a graph to mix the current gradients. We further prove that the metric controls the convergence rate, particularly in settings where the heterogeneity across nodes dominates the stochasticity between updates for a given node. Motivated by our analysis, we propose an approach that periodically and efficiently optimizes the metric using standard convex constrained optimization and sketching techniques. Through comprehensive experiments on standard computer vision and NLP benchmarks, we show that our approach leads to improvement in test performance for a wide range of tasks.

Lipschitz-constrained neural networks have many applications in machine learning. Since designing and training expressive Lipschitz-constrained networks is very challenging, there is a need for improved methods and a better theoretical understanding. Unfortunately, it turns out that ReLU networks have provable disadvantages in this setting. Hence, we propose to use learnable spline activation functions with at least 3 linear regions instead. We prove that this choice is optimal among all component-wise $1$-Lipschitz activation functions in the sense that no other weight constrained architecture can approximate a larger class of functions. Additionally, this choice is at least as expressive as the recently introduced non component-wise Groupsort activation function for spectral-norm-constrained weights. Previously published numerical results support our theoretical findings.

Out-of-distribution (OOD) detection is a critical task for deploying machine learning models in the open world. Distance-based methods have demonstrated promise, where testing samples are detected as OOD if they are relatively far away from in-distribution (ID) data. However, prior methods impose a strong distributional assumption of the underlying feature space, which may not always hold. In this paper, we explore the efficacy of non-parametric nearest-neighbor distance for OOD detection, which has been largely overlooked in the literature. Unlike prior works, our method does not impose any distributional assumption, hence providing stronger flexibility and generality. We demonstrate the effectiveness of nearest-neighbor-based OOD detection on several benchmarks and establish superior performance. Under the same model trained on ImageNet-1k, our method substantially reduces the false positive rate (FPR@TPR95) by 24.77% compared to a strong baseline SSD+, which uses a parametric approach Mahalanobis distance in detection.

We propose the use of low bit-depth Sigma-Delta and distributed noise-shaping methods for quantizing the Random Fourier features (RFFs) associated with shift-invariant kernels. We prove that our quantized RFFs -- even in the case of $1$-bit quantization -- allow a high accuracy approximation of the underlying kernels, and the approximation error decays at least polynomially fast as the dimension of the RFFs increases. We also show that the quantized RFFs can be further compressed, yielding an excellent trade-off between memory use and accuracy. Namely, the approximation error now decays exponentially as a function of the bits used. Moreover, we empirically show by testing the performance of our methods on several machine learning tasks that our method compares favorably to other state of the art quantization methods in this context.

Efficient anomaly detection and diagnosis in multivariate time-series data is of great importance for modern industrial applications. However, building a system that is able to quickly and accurately pinpoint anomalous observations is a challenging problem. This is due to the lack of anomaly labels, high data volatility and the demands of ultra-low inference times in modern applications. Despite the recent developments of deep learning approaches for anomaly detection, only a few of them can address all of these challenges. In this paper, we propose TranAD, a deep transformer network based anomaly detection and diagnosis model which uses attention-based sequence encoders to swiftly perform inference with the knowledge of the broader temporal trends in the data. TranAD uses focus score-based self-conditioning to enable robust multi-modal feature extraction and adversarial training to gain stability. Additionally, model-agnostic meta learning (MAML) allows us to train the model using limited data. Extensive empirical studies on six publicly available datasets demonstrate that TranAD can outperform state-of-the-art baseline methods in detection and diagnosis performance with data and time-efficient training. Specifically, TranAD increases F1 scores by up to 17%, reducing training times by up to 99% compared to the baselines.

Intrusion detection has been a key topic in the field of cyber security, and the common network threats nowadays have the characteristics of varieties and variation. Considering the serious imbalance of intrusion detection datasets will result in low classification performance on attack behaviors of small sample size and difficulty to detect network attacks accurately and efficiently, using Adaptive Synthetic Sampling (ADASYN) method to balance datasets was proposed in this paper. In addition, Random Forest algorithm was used to train intrusion detection classifiers. Through the comparative experiment of Intrusion detection on CICIDS 2017 dataset, it is found that ADASYN with Random Forest performs better. Based on the experimental results, the improvement of precision, recall, F1 scores and AUC values after ADASYN is then analyzed. Experiments show that the proposed method can be applied to intrusion detection with large data, and can effectively improve the classification accuracy of network attack behaviors. Compared with traditional machine learning models, it has better performance, generalization ability and robustness.

Sample efficiency is crucial for imitation learning methods to be applicable in real-world applications. Many studies improve sample efficiency by extending adversarial imitation to be off-policy regardless of the fact that these off-policy extensions could either change the original objective or involve complicated optimization. We revisit the foundation of adversarial imitation and propose an off-policy sample efficient approach that requires no adversarial training or min-max optimization. Our formulation capitalizes on two key insights: (1) the similarity between the Bellman equation and the stationary state-action distribution equation allows us to derive a novel temporal difference (TD) learning approach; and (2) the use of a deterministic policy simplifies the TD learning. Combined, these insights yield a practical algorithm, Deterministic and Discriminative Imitation (D2-Imitation), which operates by first partitioning samples into two replay buffers and then learning a deterministic policy via off-policy reinforcement learning. Our empirical results show that D2-Imitation is effective in achieving good sample efficiency, outperforming several off-policy extension approaches of adversarial imitation on many control tasks.

The switch from a Model-Centric to a Data-Centric mindset is putting emphasis on data and its quality rather than algorithms, bringing forward new challenges. In particular, the sensitive nature of the information in highly regulated scenarios needs to be accounted for. Specific approaches to address the privacy issue have been developed, as Privacy Enhancing Technologies. However, they frequently cause loss of information, putting forward a crucial trade-off among data quality and privacy. A clever way to bypass such a conundrum relies on Synthetic Data: data obtained from a generative process, learning the real data properties. Both Academia and Industry realized the importance of evaluating synthetic data quality: without all-round reliable metrics, the innovative data generation task has no proper objective function to maximize. Despite that, the topic remains under-explored. For this reason, we systematically catalog the important traits of synthetic data quality and privacy, and devise a specific methodology to test them. The result is DAISYnt (aDoption of Artificial Intelligence SYnthesis): a comprehensive suite of advanced tests, which sets a de facto standard for synthetic data evaluation. As a practical use-case, a variety of generative algorithms have been trained on real-world Credit Bureau Data. The best model has been assessed, using DAISYnt on the different synthetic replicas. Further potential uses, among others, entail auditing and fine-tuning of generative models or ensuring high quality of a given synthetic dataset. From a prescriptive viewpoint, eventually, DAISYnt may pave the way to synthetic data adoption in highly regulated domains, ranging from Finance to Healthcare, through Insurance and Education.

User interests are usually dynamic in the real world, which poses both theoretical and practical challenges for learning accurate preferences from rich behavior data. Among existing user behavior modeling solutions, attention networks are widely adopted for its effectiveness and relative simplicity. Despite being extensively studied, existing attentions still suffer from two limitations: i) conventional attentions mainly take into account the spatial correlation between user behaviors, regardless the distance between those behaviors in the continuous time space; and ii) these attentions mostly provide a dense and undistinguished distribution over all past behaviors then attentively encode them into the output latent representations. This is however not suitable in practical scenarios where a user's future actions are relevant to a small subset of her/his historical behaviors. In this paper, we propose a novel attention network, named self-modulating attention, that models the complex and non-linearly evolving dynamic user preferences. We empirically demonstrate the effectiveness of our method on top-N sequential recommendation tasks, and the results on three large-scale real-world datasets show that our model can achieve state-of-the-art performance.

The use of Machine Learning (ML) has rapidly spread across several fields, having encountered many applications in Structural Dynamics and Vibroacoustic (SD\&V). The increasing capabilities of ML to unveil insights from data, driven by unprecedented data availability, algorithms advances and computational power, enhance decision making, uncertainty handling, patterns recognition and real-time assessments. Three main applications in SD\&V have taken advantage of these benefits. In Structural Health Monitoring, ML detection and prognosis lead to safe operation and optimized maintenance schedules. System identification and control design are leveraged by ML techniques in Active Noise Control and Active Vibration Control. Finally, the so-called ML-based surrogate models provide fast alternatives to costly simulations, enabling robust and optimized product design. Despite the many works in the area, they have not been reviewed and analyzed. Therefore, to keep track and understand this ongoing integration of fields, this paper presents a survey of ML applications in SD\&V analyses, shedding light on the current state of implementation and emerging opportunities. The main methodologies, advantages, limitations, and recommendations based on scientific knowledge were identified for each of the three applications. Moreover, the paper considers the role of Digital Twins and Physics Guided ML to overcome current challenges and power future research progress. As a result, the survey provides a broad overview of the present landscape of ML applied in SD\&V and guides the reader to an advanced understanding of progress and prospects in the field.

We apply a generalized piecewise-linear (PL) version of Morse theory due to Grunert-Kuhnel-Rote to define and study new local and global notions of topological complexity for fully-connected feedforward ReLU neural network functions, F: R^n -> R. Along the way, we show how to construct, for each such F, a canonical polytopal complex K(F) and a deformation retract of the domain onto K(F), yielding a convenient compact model for performing calculations. We also give a combinatorial description of local complexity for depth 2 networks, and a construction showing that local complexity can be arbitrarily high.

Common wisdom in the graph neural network (GNN) community dictates that anisotropic models -- in which messages sent between nodes are a function of both the source and target node -- are required to achieve state-of-the-art performance. Benchmarks to date have demonstrated that these models perform better than comparable isotropic models -- where messages are a function of the source node only. In this work we provide empirical evidence challenging this narrative: we propose an isotropic GNN, which we call Efficient Graph Convolution (EGC), that consistently outperforms comparable anisotropic models, including the popular GAT or PNA architectures by using spatially-varying adaptive filters. In addition to raising important questions for the GNN community, our work has significant real-world implications for efficiency. EGC achieves higher model accuracy, with lower memory consumption and latency, along with characteristics suited to accelerator implementation, while being a drop-in replacement for existing architectures. As an isotropic model, it requires memory proportional to the number of vertices in the graph ($\mathcal{O}(V)$); in contrast, anisotropic models require memory proportional to the number of edges ($\mathcal{O}(E)$). We demonstrate that EGC outperforms existing approaches across 6 large and diverse benchmark datasets, and conclude by discussing questions that our work raise for the community going forward. Code and pretrained models for our experiments are provided at https://github.com/shyam196/egc.

Random field Monte Carlo (MC) reliability analysis is a robust stochastic method to determine the probability of failure. This method, however, requires a large number of numerical simulations demanding high computational costs. This paper explores the efficiency of different machine learning (ML) algorithms used as surrogate models trained on a limited number of random field slope stability simulations in predicting the results of large datasets. The MC data in this paper require only the examination of failure or non-failure, circumventing the time-consuming calculation of factors of safety. An extensive dataset is generated, consisting of 120,000 finite difference MC slope stability simulations incorporating different levels of soil heterogeneity and anisotropy. The Bagging Ensemble, Random Forest and Support Vector classifiers are found to be the superior models for this problem amongst 9 different models and ensemble classifiers. Trained only on 0.47% of data (500 samples), the ML model can classify the entire 120,000 samples with an accuracy of %85 and AUC score of %91. The performance of ML methods in classifying the random field slope stability results generally reduces with higher anisotropy and heterogeneity of soil. The ML assisted MC reliability analysis proves a robust stochastic method where errors in the predicted probability of failure using %5 of MC data is only %0.46 in average. The approach reduced the computational time from 306 days to less than 6 hours.

The vanishing ideal of a set of points $X\subseteq \mathbb{R}^n$ is the set of polynomials that evaluate to $0$ over all points $\mathbf{x} \in X$ and admits an efficient representation by a finite set of polynomials called generators. To accommodate the noise in the data set, we introduce the Conditional Gradients Approximately Vanishing Ideal algorithm (CGAVI) for the construction of the set of generators of the approximately vanishing ideal. The constructed set of generators captures polynomial structures in data and gives rise to a feature map that can, for example, be used in combination with a linear classifier for supervised learning. In CGAVI, we construct the set of generators by solving specific instances of (constrained) convex optimization problems with the Pairwise Frank-Wolfe algorithm (PFW). Among other things, the constructed generators inherit the LASSO generalization bound and not only vanish on the training but also on out-sample data. Moreover, CGAVI admits a compact representation of the approximately vanishing ideal by constructing few generators with sparse coefficient vectors.

The therapeutic working alliance is an important predictor of the outcome of the psychotherapy treatment. In practice, the working alliance is estimated from a set of scoring questionnaires in an inventory that both the patient and the therapists fill out. In this work, we propose an analytical framework of directly inferring the therapeutic working alliance from the natural language within the psychotherapy sessions in a turn-level resolution with deep embeddings such as the Doc2Vec and SentenceBERT models. The transcript of each psychotherapy session can be transcribed and generated in real-time from the session speech recordings, and these embedded dialogues are compared with the distributed representations of the statements in the working alliance inventory. We demonstrate, in a real-world dataset with over 950 sessions of psychotherapy treatments in anxiety, depression, schizophrenia and suicidal patients, the effectiveness of this method in mapping out trajectories of patient-therapist alignment and the interpretability that can offer insights in clinical psychiatry. We believe such a framework can be provide timely feedback to the therapist regarding the quality of the conversation in interview sessions.

Stochastic partial differential equations (SPDEs) are significant tools for modeling dynamics in many areas including atmospheric sciences and physics. Neural Operators, generations of neural networks with capability of learning maps between infinite-dimensional spaces, are strong tools for solving parametric PDEs. However, they lack the ability to modeling SPDEs which usually have poor regularity due to the driving noise. As the theory of regularity structure has achieved great successes in analyzing SPDEs and provides the concept model feature vectors that well-approximate SPDEs' solutions, we propose the Neural Operator with Regularity Structure (NORS) which incorporates the feature vectors for modeling dynamics driven by SPDEs. We conduct experiments on various of SPDEs including the dynamic Phi41 model and the 2d stochastic Navier-Stokes equation, and the results demonstrate that the NORS is resolution-invariant, efficient, and achieves one order of magnitude lower error with a modest amount of data.

Multi-label learning is often used to mine the correlation between variables and multiple labels, and its research focuses on fully extracting the information between variables and labels. The $\ell_{2,1}$ regularization is often used to get a sparse coefficient matrix, but the problem of multicollinearity among variables cannot be effectively solved. In this paper, the proposed model can choose the most relevant variables by solving a joint constraint optimization problem using the $\ell_{2,1}$ regularization and Frobenius regularization. In manifold regularization, we carry out a random walk strategy based on the joint structure to construct a neighborhood graph, which is highly robust to outliers. In addition, we give an iterative algorithm of the proposed method and proved the convergence of this algorithm. The experiments on the real-world data sets also show that the comprehensive performance of our method is consistently better than the classical method.

With the advancements in deep learning (DL) and an increasing interest in data-driven speech processing methods, a major challenge for speech data scientists in the healthcare domain is the anonymization of pathological speech, which is a required step to be able to make them accessible as a public training resource. In this paper, we investigate pathological speech data and compare their speaker verifiability with that of healthy individuals. We utilize a large pathological speech corpus of more than 2,000 test subjects with various speech and voice disorders from different ages and apply DL-based automatic speaker verification (ASV) techniques. As a result, we obtained a mean equal error rate (EER) of 0.86% with a standard deviation of 0.16%, which is a factor of three lower than comparable healthy speech databases. We further perform detailed analyses of external influencing factors on ASV such as age, pathology, recording environment, and utterance length, to explore their respective effect. Our findings indicate that speech pathology is a potential biomarker in ASV. This is potentially of high interest for the anonymization of pathological speech data.

Recently, successful applications of reinforcement learning to chip placement have emerged. Pretrained models are necessary to improve efficiency and effectiveness. Currently, the weights of objective metrics (e.g., wirelength, congestion, and timing) are fixed during pretraining. However, fixed-weighed models cannot generate the diversity of placements required for engineers to accommodate changing requirements as they arise. This paper proposes flexible multiple-objective reinforcement learning (MORL) to support objective functions with inference-time variable weights using just a single pretrained model. Our macro placement results show that MORL can generate the Pareto frontier of multiple objectives effectively.

Effective decision, exploration, and adaptation often require an agent to know what it knows and, also, what it does not know. This capability relies on the quality of \textit{joint} predictions of labels assigned to multiple inputs. Conventional neural networks lack this capability and, since most research has focused on marginal predictions, this shortcoming has been largely overlooked. By assessing the quality of joint predictions it is possible to determine whether a neural network effectively distinguishes between epistemic uncertainty (that due to lack of knowledge) and aleatoric uncertainty (that due to chance). We introduce the \textit{epistemic neural network} (ENN) as a general interface for uncertainty modeling in deep learning. While prior approaches to uncertainty modeling can be viewed as ENNs, the new interface facilitates comparison of joint predictions, and the design of novel architectures and algorithms. In particular, we introduce the \textit{epinet}: an architecture that can supplement any existing neural network, including pretrained models, and trained with modest incremental computation to represent uncertainty. With an epinet, conventional neural networks outperform very large ensembles, consisting of hundreds or more particles, with orders of magnitude less computation. We demonstrate this efficacy across synthetic data, ImageNet, and sequential decision problems. As part of this effort we open-source experiment code.  ( 2 min )

The click-through rate (CTR) prediction task is to predict whether a user will click on the recommended item. As mind-boggling amounts of data are produced online daily, accelerating CTR prediction model training is critical to ensuring an up-to-date model and reducing the training cost. One approach to increase the training speed is to apply large batch training. However, as shown in computer vision and natural language processing tasks, training with a large batch easily suffers from the loss of accuracy. Our experiments show that previous scaling rules fail in the training of CTR prediction neural networks. To tackle this problem, we first theoretically show that different frequencies of ids make it challenging to scale hyperparameters when scaling the batch size. To stabilize the training process in a large batch size setting, we develop the adaptive Column-wise Clipping (CowClip). It enables an easy and effective scaling rule for the embeddings, which keeps the learning rate unchanged and scales the L2 loss. We conduct extensive experiments with four CTR prediction networks on two real-world datasets and successfully scaled 128 times the original batch size without accuracy loss. In particular, for CTR prediction model DeepFM training on the Criteo dataset, our optimization framework enlarges the batch size from 1K to 128K with over 0.1% AUC improvement and reduces training time from 12 hours to 10 minutes on a single V100 GPU. Our code locates at https://github.com/zhengzangw/LargeBatchCTR.

Finding the most influential nodes in a network is a computationally hard problem with several possible applications in various kinds of network-based problems. While several methods have been proposed for tackling the influence maximisation (IM) problem, their runtime typically scales poorly when the network size increases. Here, we propose an original method, based on network downscaling, that allows a multi-objective evolutionary algorithm (MOEA) to solve the IM problem on a reduced scale network, while preserving the relevant properties of the original network. The downscaled solution is then upscaled to the original network, using a mechanism based on centrality metrics such as PageRank. Our results on eight large networks (including two with $\sim$50k nodes) demonstrate the effectiveness of the proposed method with a more than 10-fold runtime gain compared to the time needed on the original network, and an up to $82\%$ time reduction compared to CELF.

Decision trees are considered one of the most powerful tools for data classification. Accelerating the decision tree search is crucial for on-the-edge applications that have limited power and latency budget. In this paper, we propose a Content Addressable Memory (CAM) Compiler for Decision Tree (DT) inference acceleration. We propose a novel "adaptive-precision" scheme that results in a compact implementation and enables an efficient bijective mapping to Ternary Content Addressable Memories while maintaining high inference accuracies. In addition, a Resistive-CAM (ReCAM) functional synthesizer is developed for mapping the decision tree to the ReCAM and performing functional simulations for energy, latency, and accuracy evaluations. We study the decision tree accuracy under hardware non-idealities including device defects, manufacturing variability, and input encoding noise. We test our framework on various DT datasets including \textit{Give Me Some Credit}, \textit{Titanic}, and \textit{COVID-19}. Our results reveal up to {42.4\%} energy savings and up to 17.8x better energy-delay-area product compared to the state-of-art hardware accelerators, and up to 333 million decisions per sec for the pipelined implementation.

Hypergraph Convolutional Neural Networks (HGCNNs) have demonstrated their potential in modeling high-order relations preserved in graph-structured data. However, most existing convolution filters are localized and determined by the pre-defined initial hypergraph topology, neglecting to explore implicit and long-range relations in real-world data. In this paper, we propose the first learning-based method tailored for constructing adaptive hypergraph structure, termed HypERgrAph Laplacian aDaptor (HERALD), which serves as a generic plug-and-play module for improving the representational power of HGCNNs.Specifically, HERALD adaptively optimizes the adjacency relationship between vertices and hyperedges in an end-to-end manner and thus the task-aware hypergraph is learned. Furthermore, HERALD employs the self-attention mechanism to capture the non-local paired-nodes relation. Extensive experiments on various popular hypergraph datasets for node classification and graph classification tasks demonstrate that our approach obtains consistent and considerable performance enhancement, proving its effectiveness and generalization ability.

Code is seldom written in a single left-to-right pass and is instead repeatedly edited and refined. We introduce InCoder, a unified generative model that can perform program synthesis (via left-to-right generation) as well as editing (via infilling). InCoder is trained to generate code files from a large corpus of permissively licensed code, where regions of code have been randomly masked and moved to the end of each file, allowing code infilling with bidirectional context. Our model is the first generative model that is able to directly perform zero-shot code infilling, which we evaluate on challenging tasks such as type inference, comment generation, and variable re-naming. We find that the ability to condition on bidirectional context substantially improves performance on these tasks, while still performing comparably on standard program synthesis benchmarks in comparison to left-to-right only models pretrained at similar scale. The InCoder models and code are publicly released. https://sites.google.com/view/incoder-code-models

Novel and high-performance medical image classification pipelines are heavily utilizing ensemble learning strategies. The idea of ensemble learning is to assemble diverse models or multiple predictions and, thus, boost prediction performance. However, it is still an open question to what extent as well as which ensemble learning strategies are beneficial in deep learning based medical image classification pipelines. In this work, we proposed a reproducible medical image classification pipeline for analyzing the performance impact of the following ensemble learning techniques: Augmenting, Stacking, and Bagging. The pipeline consists of state-of-the-art preprocessing and image augmentation methods as well as 9 deep convolution neural network architectures. It was applied on four popular medical imaging datasets with varying complexity. Furthermore, 12 pooling functions for combining multiple predictions were analyzed, ranging from simple statistical functions like unweighted averaging up to more complex learning-based functions like support vector machines. Our results revealed that Stacking achieved the largest performance gain of up to 13% F1-score increase. Augmenting showed consistent improvement capabilities by up to 4% and is also applicable to single model based pipelines. Cross-validation based Bagging demonstrated significant performance gain close to Stacking, which resulted in an F1-score increase up to +11%. Furthermore, we demonstrated that simple statistical pooling functions are equal or often even better than more complex pooling functions. We concluded that the integration of ensemble learning techniques is a powerful method for any medical image classification pipeline to improve robustness and boost performance.

Next Point-of-Interest (POI) recommendation has become an indispensable functionality in Location-based Social Networks (LBSNs) due to its effectiveness in helping people decide the next POI to visit. However, accurate recommendation requires a vast amount of historical check-in data, thus threatening user privacy as the location-sensitive data needs to be handled by cloud servers. Although there have been several on-device frameworks for privacy-preserving POI recommendations, they are still resource-intensive when it comes to storage and computation, and show limited robustness to the high sparsity of user-POI interactions. On this basis, we propose a novel decentralized collaborative learning framework for POI recommendation (DCLR), which allows users to train their personalized models locally in a collaborative manner. DCLR significantly reduces the local models' dependence on the cloud for training, and can be used to expand arbitrary centralized recommendation models. To counteract the sparsity of on-device user data when learning each local model, we design two self-supervision signals to pretrain the POI representations on the server with geographical and categorical correlations of POIs. To facilitate collaborative learning, we innovatively propose to incorporate knowledge from either geographically or semantically similar users into each local model with attentive aggregation and mutual information maximization. The collaborative learning process makes use of communications between devices while requiring only minor engagement from the central server for identifying user groups, and is compatible with common privacy preservation mechanisms like differential privacy.  ( 2 min )

How to effectively sample high-quality negative instances is important for well training a recommendation model. We argue that a high-quality negative should be both \textit{informativeness} and \textit{unbiasedness}. Although previous studies have proposed some approaches to address the informativeness in negative sampling, few has been done to discriminating false negative from true negative for unbiased negative sampling, not to mention taking both into consideration. This paper first adopts a parameter learning perspective to analyze negative informativeness and unbiasedness in loss gradient-based model training. We argue that both negative sampling and collaborative filtering include an implicit task of negative classification, from which we report an insightful yet beneficial finding about the order relation in predicted negatives' scores. Based on our finding and by regarding negatives as random variables, we next derive the class condition density of true negatives and that of false negatives. We also design a Bayesian classifier for negative classification, from which we define a quantitative unbiasedness measure for negatives. Finally, we propose to use a harmonic mean of informativeness and unbiasedness to sample high-quality negatives. Experimental studies validate the superiority of our negative sampling algorithm over the peers in terms of better sampling quality and better recommendation performance.  ( 2 min )

CNN image classifiers are widely used, thanks to their efficiency and accuracy. However, they can suffer from biases that impede their practical applications. Most existing bias investigation techniques are either inapplicable to general image classification tasks or require significant user efforts in perusing all data subgroups to manually specify which data attributes to inspect. We present VisCUIT, an interactive visualization system that reveals how and why a CNN classifier is biased. VisCUIT visually summarizes the subgroups on which the classifier underperforms and helps users discover and characterize the cause of the underperformances by revealing image concepts responsible for activating neurons that contribute to misclassifications. VisCUIT runs in modern browsers and is open-source, allowing people to easily access and extend the tool to other model architectures and datasets. VisCUIT is available at the following public demo link: https://poloclub.github.io/VisCUIT. A video demo is available at https://youtu.be/eNDbSyM4R_4.  ( 2 min )

Coverage and capacity are the important metrics for performance evaluation in wireless networks, while the coverage and capacity have several conflicting relationships, e.g. high transmit power contributes to large coverage but high inter-cell interference reduces the capacity performance. Therefore, in order to strike a balance between the coverage and capacity, a novel model is proposed for the coverage and capacity optimization of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) assisted networks. To solve the coverage and capacity optimization (CCO) problem, a machine learning-based multi-objective optimization algorithm, i.e., the multi-objective proximal policy optimization (MO-PPO) algorithm, is proposed. In this algorithm, a loss function-based update strategy is the core point, which is able to calculate weights for both loss functions of coverage and capacity by a min-norm solver at each update. The numerical results demonstrate that the investigated update strategy outperforms the fixed weight-based MO algorithms.  ( 2 min )

Structured and grounded representation of text is typically formalized by closed information extraction, the problem of extracting an exhaustive set of (subject, relation, object) triplets that are consistent with a predefined set of entities and relations from a knowledge base schema. Most existing works are pipelines prone to error accumulation, and all approaches are only applicable to unrealistically small numbers of entities and relations. We introduce GenIE (generative information extraction), the first end-to-end autoregressive formulation of closed information extraction. GenIE naturally exploits the language knowledge from the pre-trained transformer by autoregressively generating relations and entities in textual form. Thanks to a new bi-level constrained generation strategy, only triplets consistent with the predefined knowledge base schema are produced. Our experiments show that GenIE is state-of-the-art on closed information extraction, generalizes from fewer training data points than baselines, and scales to a previously unmanageable number of entities and relations. With this work, closed information extraction becomes practical in realistic scenarios, providing new opportunities for downstream tasks. Finally, this work paves the way towards a unified end-to-end approach to the core tasks of information extraction. Code, data and models available at https://github.com/epfl-dlab/GenIE.  ( 2 min )

In this paper, we develop an efficient nonparametric Bayesian estimation of the kernel function of Hawkes processes. The non-parametric Bayesian approach is important because it provides flexible Hawkes kernels and quantifies their uncertainty. Our method is based on the cluster representation of Hawkes processes. Utilizing the finite support assumption of the Hawkes process, we efficiently sample random branching structures and thus, we split the Hawkes process into clusters of Poisson processes. We derive two algorithms -- a block Gibbs sampler and a maximum a posteriori estimator based on expectation maximization -- and we show that our methods have a linear time complexity, both theoretically and empirically. On synthetic data, we show our methods to be able to infer flexible Hawkes triggering kernels. On two large-scale Twitter diffusion datasets, we show that our methods outperform the current state-of-the-art in goodness-of-fit and that the time complexity is linear in the size of the dataset. We also observe that on diffusions related to online videos, the learned kernels reflect the perceived longevity for different content types such as music or pets videos.  ( 2 min )

Prompt-based learning (i.e., prompting) is an emerging paradigm for exploiting knowledge learned by a pretrained language model. In this paper, we propose Automatic Multi-Label Prompting (AMuLaP), a simple yet effective method to automatically select label mappings for few-shot text classification with prompting. Our method exploits one-to-many label mappings and a statistics-based algorithm to select label mappings given a prompt template. Our experiments demonstrate that AMuLaP achieves competitive performance on the GLUE benchmark without human effort or external resources.  ( 2 min )

Hyperspectral images encode rich structure that can be exploited for material discrimination by machine learning algorithms. This article introduces the Active Diffusion and VCA-Assisted Image Segmentation (ADVIS) for active material discrimination. ADVIS selects high-purity, high-density pixels that are far in diffusion distance (a data-dependent metric) from other high-purity, high-density pixels in the hyperspectral image. The ground truth labels of these pixels are queried and propagated to the rest of the image. The ADVIS active learning algorithm is shown to strongly outperform its fully unsupervised clustering algorithm counterpart, suggesting that the incorporation of a very small number of carefully-selected ground truth labels can result in substantially superior material discrimination in hyperspectral images.  ( 2 min )

Structured pruning efficiently compresses networks by identifying and removing unimportant neurons. While this can be elegantly achieved by applying sparsity-inducing regularisation on BatchNorm parameters, an L1 penalty would shrink all scaling factors rather than just those of superfluous neurons. To tackle this issue, we introduce a simple BatchNorm variation with bounded scaling parameters, based on which we design a novel regularisation term that suppresses only neurons with low importance. Under our method, the weights of unnecessary neurons effectively recede, producing a polarised bimodal distribution of importances. We show that neural networks trained this way can be pruned to a larger extent and with less deterioration. We one-shot prune VGG and ResNet architectures at different ratios on CIFAR and ImagenNet datasets. In the case of VGG-style networks, our method significantly outperforms existing approaches particularly under a severe pruning regime.  ( 2 min )

An important property for deep neural networks is the ability to perform robust out-of-distribution detection on previously unseen data. This property is essential for safety purposes when deploying models for real world applications. Recent studies show that probabilistic generative models can perform poorly on this task, which is surprising given that they seek to estimate the likelihood of training data. To alleviate this issue, we propose the exponentially tilted Gaussian prior distribution for the Variational Autoencoder (VAE) which pulls points onto the surface of a hyper-sphere in latent space. This achieves state-of-the art results on the area under the curve-receiver operator characteristics metric using just the log-likelihood that the VAE naturally assigns. Because this prior is a simple modification of the traditional VAE prior, it is faster and easier to implement than competitive methods.  ( 2 min )

Named Entity Recognition (NER) is a basic NLP task and finds major applications in conversational and search systems. It helps us identify key entities in a sentence used for the downstream application. NER or similar slot filling systems for popular languages have been heavily used in commercial applications. In this work, we focus on Marathi, an Indian language, spoken prominently by the people of Maharashtra state. Marathi is a low resource language and still lacks useful NER resources. We present L3Cube-MahaNER, the first major gold standard named entity recognition dataset in Marathi. We also describe the manual annotation guidelines followed during the process. In the end, we benchmark the dataset on different CNN, LSTM, and Transformer based models like mBERT, XLM-RoBERTa, IndicBERT, MahaBERT, etc. The MahaBERT provides the best performance among all the models. The data and models are available at https://github.com/l3cube-pune/MarathiNLP .

The insurance industry, with its large datasets, is a natural place to use big data solutions. However it must be stressed, that significant number of applications for machine learning in insurance industry, like fraud detection or claim prediction, deals with the problem of machine learning on an imbalanced data set. This is due to the fact that frauds or claims are rare events when compared with the entire population of drivers. The problem of imbalanced learning is often hard to overcome. Therefore, the main goal of this work is to present and apply various methods of dealing with an imbalanced dataset in the context of claim occurrence prediction in car insurance. In addition, the above techniques are used to compare the results of machine learning algorithms in the context of claim occurrence prediction in car insurance. Our study covers the following techniques: logistic-regression, decision tree, random forest, xgBoost, feed-forward network. The problem is the classification one.

Deep learning models have been efficient lately on image parsing tasks. However, deep learning models are not fully capable of exploiting visual and contextual information simultaneously. The proposed three-layer context-based deep architecture is capable of integrating context explicitly with visual information. The novel idea here is to have a visual layer to learn visual characteristics from binary class-based learners, a contextual layer to learn context, and then an integration layer to learn from both via genetic algorithm-based optimal fusion to produce a final decision. The experimental outcomes when evaluated on benchmark datasets are promising. Further analysis shows that optimized network weights can improve performance and make stable predictions.

Many software systems today face uncertain operating conditions, such as sudden changes in the availability of resources or unexpected user behavior. Without proper mitigation these uncertainties can jeopardize the system goals. Self-adaptation is a common approach to tackle such uncertainties. When the system goals may be compromised, the self-adaptive system has to select the best adaptation option to reconfigure by analyzing the possible adaptation options, i.e., the adaptation space. Yet, analyzing large adaptation spaces using rigorous methods can be resource- and time-consuming, or even be infeasible. One approach to tackle this problem is by using online machine learning to reduce adaptation spaces. However, existing approaches require domain expertise to perform feature engineering to define the learner, and support online adaptation space reduction only for specific goals. To tackle these limitations, we present 'Deep Learning for Adaptation Space Reduction Plus' -- DLASeR+ in short. DLASeR+ offers an extendable learning framework for online adaptation space reduction that does not require feature engineering, while supporting three common types of adaptation goals: threshold, optimization, and set-point goals. We evaluate DLASeR+ on two instances of an Internet-of-Things application with increasing sizes of adaptation spaces for different combinations of adaptation goals. We compare DLASeR+ with a baseline that applies exhaustive analysis and two state-of-the-art approaches for adaptation space reduction that rely on learning. Results show that DLASeR+ is effective with a negligible effect on the realization of the adaptation goals compared to an exhaustive analysis approach, and supports three common types of adaptation goals beyond the state-of-the-art approaches.

The field of Deep Learning (DL) has undergone explosive growth during the last decade, with a substantial impact on Natural Language Processing (NLP) as well. Yet, as with other fields employing DL techniques, there has been a lack of common experimental standards compared to more established disciplines. Starting from fundamental scientific principles, we distill ongoing discussions on experimental standards in DL into a single, widely-applicable methodology. Following these best practices is crucial to strengthening experimental evidence, improve reproducibility and enable scientific progress. These standards are further collected in a public repository to help them transparently adapt to future needs.

The switch from a Model-Centric to a Data-Centric mindset is putting emphasis on data and its quality rather than algorithms, bringing forward new challenges. In particular, the sensitive nature of the information in highly regulated scenarios needs to be accounted for. Specific approaches to address the privacy issue have been developed, as Privacy Enhancing Technologies. However, they frequently cause loss of information, putting forward a crucial trade-off among data quality and privacy. A clever way to bypass such a conundrum relies on Synthetic Data: data obtained from a generative process, learning the real data properties. Both Academia and Industry realized the importance of evaluating synthetic data quality: without all-round reliable metrics, the innovative data generation task has no proper objective function to maximize. Despite that, the topic remains under-explored. For this reason, we systematically catalog the important traits of synthetic data quality and privacy, and devise a specific methodology to test them. The result is DAISYnt (aDoption of Artificial Intelligence SYnthesis): a comprehensive suite of advanced tests, which sets a de facto standard for synthetic data evaluation. As a practical use-case, a variety of generative algorithms have been trained on real-world Credit Bureau Data. The best model has been assessed, using DAISYnt on the different synthetic replicas. Further potential uses, among others, entail auditing and fine-tuning of generative models or ensuring high quality of a given synthetic dataset. From a prescriptive viewpoint, eventually, DAISYnt may pave the way to synthetic data adoption in highly regulated domains, ranging from Finance to Healthcare, through Insurance and Education.

Decentralized learning provides an effective framework to train machine learning models with data distributed over arbitrary communication graphs. However, most existing approaches toward decentralized learning disregard the interaction between data heterogeneity and graph topology. In this paper, we characterize the dependence of convergence on the relationship between the mixing weights of the graph and the data heterogeneity across nodes. We propose a metric that quantifies the ability of a graph to mix the current gradients. We further prove that the metric controls the convergence rate, particularly in settings where the heterogeneity across nodes dominates the stochasticity between updates for a given node. Motivated by our analysis, we propose an approach that periodically and efficiently optimizes the metric using standard convex constrained optimization and sketching techniques. Through comprehensive experiments on standard computer vision and NLP benchmarks, we show that our approach leads to improvement in test performance for a wide range of tasks.

Detailed feature investigations and comparisons across climates, continents and time series types can progress our understanding and modelling ability of the Earth's hydroclimate and its dynamics. As a step towards these important directions, we here propose and extensively apply a multifaceted and engineering-friendly methodological framework for the thorough characterization of seasonal hydroclimatic dependence, variability and change at the global scale. We apply this framework using over 13 000 quarterly temperature, precipitation and river flow time series. In these time series, the seasonal hydroclimatic behaviour is represented by 3-month means of earth-observed variables. In our analyses, we also adopt the well-established Koppen-Geiger climate classification system and define continental-scale regions with large or medium density of observational stations. In this context, we provide in parallel seasonal hydroclimatic feature summaries and comparisons in terms of autocorrelation, seasonality, temporal variation, entropy, long-range dependence and trends. We find notable differences to characterize the magnitudes of most of these features across the various Koppen-Geiger climate classes, as well as between several continental-scale geographical regions. We, therefore, deem that the consideration of the comparative summaries could be more beneficial in water resources engineering contexts than the also provided global summaries. Lastly, we apply explainable machine learning to compare the investigated features with respect to how informative they are in explaining and predicting either the main Koppen-Geiger climate or the continental-scale region, with the entropy, long-range dependence and trend features being (roughly) found to be less informative than the remaining ones at the seasonal time scale.

In the Big Data era, with the ubiquity of geolocation sensors in particular, massive datasets exhibiting a possibly complex spatial dependence structure are becoming increasingly available. In this context, the standard probabilistic theory of statistical learning does not apply directly and guarantees of the generalization capacity of predictive rules learned from such data are left to establish. We analyze here the simple Kriging task, the flagship problem in Geostatistics: the values of a square integrable random field $X=\{X_s\}_{s\in S}$, $S\subset \mathbb{R}^2$, with unknown covariance structure are to be predicted with minimum quadratic risk, based upon observing a single realization of the spatial process at a finite number of locations $s_1,\; \ldots,\; s_n$ in $S$. Despite the connection of this minimization problem with kernel ridge regression, establishing the generalization capacity of empirical risk minimizers is far from straightforward, due to the non i.i.d. nature of the spatial data $X_{s_1},\; \ldots,\; X_{s_n}$ involved. In this article, nonasymptotic bounds of order $O_{\mathbb{P}}(1/n)$ are proved for the excess risk of a plug-in predictive rule mimicking the true minimizer in the case of isotropic stationary Gaussian processes observed at locations forming a regular grid. These theoretical results, as well as the role played by the technical conditions required to establish them, are illustrated by various numerical experiments and hopefully pave the way for further developments in statistical learning based on spatial data.

To analyse local robustness properties of deep neural networks (DNNs), we present a practical framework from a model learning perspective. Based on black-box model learning with scenario optimisation, we abstract the local behaviour of a DNN via an affine model with the probably approximately correct (PAC) guarantee. From the learned model, we can infer the corresponding PAC-model robustness property. The innovation of our work is the integration of model learning into PAC robustness analysis: that is, we construct a PAC guarantee on the model level instead of sample distribution, which induces a more faithful and accurate robustness evaluation. This is in contrast to existing statistical methods without model learning. We implement our method in a prototypical tool named DeepPAC. As a black-box method, DeepPAC is scalable and efficient, especially when DNNs have complex structures or high-dimensional inputs. We extensively evaluate DeepPAC, with 4 baselines (using formal verification, statistical methods, testing and adversarial attack) and 20 DNN models across 3 datasets, including MNIST, CIFAR-10, and ImageNet. It is shown that DeepPAC outperforms the state-of-the-art statistical method PROVERO, and it achieves more practical robustness analysis than the formal verification tool ERAN. Also, its results are consistent with existing DNN testing work like DeepGini.

This work addresses the problem of exploration in an unknown environment. For linear dynamical systems, we use an experimental design framework and introduce an online greedy policy where the control maximizes the information of the next step. In a setting with a limited number of experimental trials, our algorithm has low complexity and shows experimentally competitive performances compared to more elaborate gradient-based methods.

As machine learning models start to be used in critical applications, their vulnerabilities and brittleness become a pressing concern. Adversarial attacks are a popular framework for studying these vulnerabilities. In this work, we study the error of linear regression in the face of adversarial attacks. We provide bounds of the error in terms of the traditional risk and the parameter norm and show how these bounds can be leveraged and make it possible to use analysis from non-adversarial setups to study the adversarial risk. The usefulness of these results is illustrated by shedding light on whether or not overparameterized linear models can be adversarially robust. We show that adding features to linear models might be either a source of additional robustness or brittleness. We show that these differences appear due to scaling and how the $\ell_1$ and $\ell_2$ norms of random projections concentrate. We also show how the reformulation we propose allows for solving adversarial training as a convex optimization problem. This is then used as a tool to study how adversarial training and other regularization methods might affect the robustness of the estimated models.

An important property for deep neural networks is the ability to perform robust out-of-distribution detection on previously unseen data. This property is essential for safety purposes when deploying models for real world applications. Recent studies show that probabilistic generative models can perform poorly on this task, which is surprising given that they seek to estimate the likelihood of training data. To alleviate this issue, we propose the exponentially tilted Gaussian prior distribution for the Variational Autoencoder (VAE) which pulls points onto the surface of a hyper-sphere in latent space. This achieves state-of-the art results on the area under the curve-receiver operator characteristics metric using just the log-likelihood that the VAE naturally assigns. Because this prior is a simple modification of the traditional VAE prior, it is faster and easier to implement than competitive methods.

Polynomial chaos expansion (PCE) is a versatile tool widely used in uncertainty quantification and machine learning, but its successful application depends strongly on the accuracy and reliability of the resulting PCE-based response surface. High accuracy typically requires high polynomial degrees, demanding many training points especially in high-dimensional problems through the curse of dimensionality. So-called sparse PCE concepts work with a much smaller selection of basis polynomials compared to conventional PCE approaches and can overcome the curse of dimensionality very efficiently, but have to pay specific attention to their strategies of choosing training points. Furthermore, the approximation error resembles an uncertainty that most existing PCE-based methods do not estimate. In this study, we develop and evaluate a fully Bayesian approach to establish the PCE representation via joint shrinkage priors and Markov chain Monte Carlo. The suggested Bayesian PCE model directly aims to solve the two challenges named above: achieving a sparse PCE representation and estimating uncertainty of the PCE itself. The embedded Bayesian regularizing via the joint shrinkage prior allows using higher polynomial degrees for given training points due to its ability to handle underdetermined situations, where the number of considered PCE coefficients could be much larger than the number of available training points. We also explore multiple variable selection methods to construct sparse PCE expansions based on the established Bayesian representations, while globally selecting the most meaningful orthonormal polynomials given the available training data. We demonstrate the advantages of our Bayesian PCE and the corresponding sparsity-inducing methods on several benchmarks.

Synthetic data generation has proven to be a promising solution for addressing data availability issues in various domains. Even more challenging is the generation of synthetic time series data, where one has to preserve temporal dynamics, i.e., the generated time series must respect the original relationships between variables across time. Recently proposed techniques such as generative adversarial networks (GANs) and quantum-GANs lack the ability to attend to the time series specific temporal correlations adequately. We propose using the inherent nature of quantum computers to simulate quantum dynamics as a technique to encode such features. We start by assuming that a given time series can be generated by a quantum process, after which we proceed to learn that quantum process using quantum machine learning. We then use the learned model to generate out-of-sample time series and show that it captures unique and complex features of the learned time series. We also study the class of time series that can be modeled using this technique. Finally, we experimentally demonstrate the proposed algorithm on an 11-qubit trapped-ion quantum machine.

We consider high-dimensional multiclass classification by sparse multinomial logistic regression. Unlike binary classification, in the multiclass setup one can think about an entire spectrum of possible notions of sparsity associated with different structural assumptions on the regression coefficients matrix. We propose a computationally feasible feature selection procedure based on penalized maximum likelihood with convex penalties capturing a specific type of sparsity at hand. In particular, we consider global sparsity, double row-wise sparsity, and low-rank sparsity, and show that with the properly chosen tuning parameters the derived plug-in classifiers attain the minimax generalization error bounds (in terms of misclassification excess risk) within the corresponding classes of multiclass sparse linear classifiers. The developed approach is general and can be adapted to other types of sparsity as well.

Effective decision, exploration, and adaptation often require an agent to know what it knows and, also, what it does not know. This capability relies on the quality of \textit{joint} predictions of labels assigned to multiple inputs. Conventional neural networks lack this capability and, since most research has focused on marginal predictions, this shortcoming has been largely overlooked. By assessing the quality of joint predictions it is possible to determine whether a neural network effectively distinguishes between epistemic uncertainty (that due to lack of knowledge) and aleatoric uncertainty (that due to chance). We introduce the \textit{epistemic neural network} (ENN) as a general interface for uncertainty modeling in deep learning. While prior approaches to uncertainty modeling can be viewed as ENNs, the new interface facilitates comparison of joint predictions, and the design of novel architectures and algorithms. In particular, we introduce the \textit{epinet}: an architecture that can supplement any existing neural network, including pretrained models, and trained with modest incremental computation to represent uncertainty. With an epinet, conventional neural networks outperform very large ensembles, consisting of hundreds or more particles, with orders of magnitude less computation. We demonstrate this efficacy across synthetic data, ImageNet, and sequential decision problems. As part of this effort we open-source experiment code.

Approximate Bayesian Computation (ABC) enables statistical inference in simulator-based models whose likelihoods are difficult to calculate but easy to simulate from. ABC constructs a kernel-type approximation to the posterior distribution through an accept/reject mechanism which compares summary statistics of real and simulated data. To obviate the need for summary statistics, we directly compare empirical distributions with a Kullback-Leibler (KL) divergence estimator obtained via contrastive learning. In particular, we blend flexible machine learning classifiers within ABC to automate fake/real data comparisons. We consider the traditional accept/reject kernel as well as an exponential weighting scheme which does not require the ABC acceptance threshold. Our theoretical results show that the rate at which our ABC posterior distributions concentrate around the true parameter depends on the estimation error of the classifier. We derive limiting posterior shape results and find that, with a properly scaled exponential kernel, asymptotic normality holds. We demonstrate the usefulness of our approach on simulated examples as well as real data in the context of stock volatility estimation.

Multi-label learning is often used to mine the correlation between variables and multiple labels, and its research focuses on fully extracting the information between variables and labels. The $\ell_{2,1}$ regularization is often used to get a sparse coefficient matrix, but the problem of multicollinearity among variables cannot be effectively solved. In this paper, the proposed model can choose the most relevant variables by solving a joint constraint optimization problem using the $\ell_{2,1}$ regularization and Frobenius regularization. In manifold regularization, we carry out a random walk strategy based on the joint structure to construct a neighborhood graph, which is highly robust to outliers. In addition, we give an iterative algorithm of the proposed method and proved the convergence of this algorithm. The experiments on the real-world data sets also show that the comprehensive performance of our method is consistently better than the classical method.

Reinforcement learning algorithms are typically limited to learning a single solution for a specified task, even though diverse solutions often exist. Recent studies showed that learning a set of diverse solutions is beneficial because diversity enables robust few-shot adaptation. Although existing methods learn diverse solutions by using the mutual information as unsupervised rewards, such an approach often suffers from the bias of the gradient estimator induced by value function approximation. In this study, we propose a novel method that can learn diverse solutions without suffering the bias problem. In our method, a policy conditioned on a continuous or discrete latent variable is trained by directly maximizing the variational lower bound of the mutual information, instead of using the mutual information as unsupervised rewards as in previous studies. Through extensive experiments on robot locomotion tasks, we demonstrate that the proposed method successfully learns an infinite set of diverse solutions by learning continuous latent variables, which is more challenging than learning a finite number of solutions. Subsequently, we show that our method enables more effective few-shot adaptation compared with existing methods.

There has been an increasing interest in utilizing machine learning methods in inverse problems and imaging. Most of the work has, however, concentrated on image reconstruction problems, and the number of studies regarding the full solution of the inverse problem is limited. In this work, we study a machine learning based approach for the Bayesian inverse problem of photoacoustic tomography. We develop an approach for estimating the posterior distribution in photoacoustic tomography using an approach based on the variational autoencoder. The approach is evaluated with numerical simulations and compared to the solution of the inverse problem using a Bayesian approach.

This work introduces time-uniform analogues of confidence intervals based on the central limit theorem (CLT). Our methods take the form of confidence sequences (CS) -- sequences of confidence intervals that are uniformly valid over time. CSs provide valid inference at arbitrary stopping times, incurring no penalties for "peeking" at the data, unlike classical confidence intervals which require the sample size to be fixed in advance. Existing CSs in the literature are nonasymptotic, requiring strong assumptions on the data, while the classical (fixed-time) CLT is ubiquitous due to the weak assumptions it imposes. Our work bridges the gap by introducing time-uniform CSs that only require CLT-like assumptions. While the CLT approximates the distribution of a sample average by that of a Gaussian at a fixed sample size, we use strong invariance principles like the seminal work of Koml\'os, Major, and Tusn\'ady to uniformly approximate the entire sample average process by an implicit Brownian motion. Applying Robbins' normal mixture martingale method to this Brownian motion then yields closed-form time-uniform boundaries. We combine these boundaries with doubly robust estimators to derive nonparametric CSs for the average treatment effect (and other causal estimands). These allow randomized experiments and observational studies to be continuously monitored and adaptively stopped, all while controlling the type-I error.

We propose the use of low bit-depth Sigma-Delta and distributed noise-shaping methods for quantizing the Random Fourier features (RFFs) associated with shift-invariant kernels. We prove that our quantized RFFs -- even in the case of $1$-bit quantization -- allow a high accuracy approximation of the underlying kernels, and the approximation error decays at least polynomially fast as the dimension of the RFFs increases. We also show that the quantized RFFs can be further compressed, yielding an excellent trade-off between memory use and accuracy. Namely, the approximation error now decays exponentially as a function of the bits used. Moreover, we empirically show by testing the performance of our methods on several machine learning tasks that our method compares favorably to other state of the art quantization methods in this context.

The persistence diagram is an increasingly useful tool from Topological Data Analysis, but its use alongside typical machine learning techniques requires mathematical finesse. The most success to date has come from methods that map persistence diagrams into vector spaces, in a way which maximizes the structure preserved. This process is commonly referred to as featurization. In this paper, we describe a mathematical framework for featurization called \emph{template functions}, and we show that it addresses the problem of approximating continuous functions on compact subsets of the space of persistence diagrams. Specifically, we begin by characterizing relative compactness with respect to the bottleneck distance, and then provide explicit theoretical methods for constructing compact-open dense subsets of continuous functions on persistence diagrams. These dense subsets -- obtained via template functions -- are leveraged for supervised learning tasks with persistence diagrams. Specifically, we test the method for classification and regression algorithms on several examples including shape data and dynamical systems.

The demand of probabilistic time series forecasting has been recently raised in various dynamic system scenarios, for example, system identification and prognostic and health management of machines. To this end, we combine the advances in both deep generative models and state space model (SSM) to come up with a novel, data-driven deep probabilistic sequence model. Specifically, we follow the popular encoder-decoder generative structure to build the recurrent neural networks (RNN) assisted variational sequence model on an augmented recurrent input space, which could induce rich stochastic sequence dependency. Besides, in order to alleviate the inconsistency issue of the posterior between training and predicting as well as improving the mining of dynamic patterns, we (i) propose using a lagged hybrid output as input for the posterior at next time step, which brings training and predicting into alignment; and (ii) further devise a generalized auto-regressive strategy that encodes all the historical dependencies for the posterior. Thereafter, we first investigate the methodological characteristics of the proposed deep probabilistic sequence model on toy cases, and then comprehensively demonstrate the superiority of our model against existing deep probabilistic SSM models through extensive numerical experiments on eight system identification benchmarks from various dynamic systems. Finally, we apply our sequence model to a real-world centrifugal compressor forecasting problem, and again verify its outstanding performance by quantifying the time series predictive distribution.

In this paper, we develop an efficient nonparametric Bayesian estimation of the kernel function of Hawkes processes. The non-parametric Bayesian approach is important because it provides flexible Hawkes kernels and quantifies their uncertainty. Our method is based on the cluster representation of Hawkes processes. Utilizing the finite support assumption of the Hawkes process, we efficiently sample random branching structures and thus, we split the Hawkes process into clusters of Poisson processes. We derive two algorithms -- a block Gibbs sampler and a maximum a posteriori estimator based on expectation maximization -- and we show that our methods have a linear time complexity, both theoretically and empirically. On synthetic data, we show our methods to be able to infer flexible Hawkes triggering kernels. On two large-scale Twitter diffusion datasets, we show that our methods outperform the current state-of-the-art in goodness-of-fit and that the time complexity is linear in the size of the dataset. We also observe that on diffusions related to online videos, the learned kernels reflect the perceived longevity for different content types such as music or pets videos.

Many real-world systems are described not only by data from a single source but via multiple data views. For example, in genomic medicine, a patient can be described by data from different molecular layers. This raises the need for multi-view models that are able to disentangle variation within and across data views in an interpretable manner. Latent variable models with structured sparsity are a commonly used tool to address this modeling task but interpretability is cumbersome since it requires a direct inspection and interpretation of each factor via a specialized domain expert. Here, we propose MuVI, a novel approach for domain-informed multi-view latent variable models, facilitating the analysis of multi-view data in an inherently explainable manner. We demonstrate that our model (i) is able to integrate noisy domain expertise in form of feature sets, (ii) is robust to noise in the encoded domain knowledge, (iii) results in identifiable factors and (iv) is able to infer interpretable and biologically meaningful axes of variation in a real-world multi-view dataset of cancer patients.

Regression-based frameworks for streamflow regionalization are built around catchment attributes that traditionally originate from catchment hydrology, flood frequency analysis and their interplay. In this work, we deviated from this traditional path by formulating and extensively investigating the first regression-based streamflow regionalization frameworks that largely emerge from general-purpose time series features for data science and, more precisely, from a large variety of such features. We focused on 28 features that included (partial) autocorrelation, entropy, temporal variation, seasonality, trend, lumpiness, stability, nonlinearity, linearity, spikiness, curvature and others. We estimated these features for daily temperature, precipitation and streamflow time series from 511 catchments, and then merged them within regionalization contexts with traditional topographic, land cover, soil and geologic attributes. Precipitation and temperature features (e.g., the spectral entropy, seasonality strength and lag-1 autocorrelation of the precipitation time series, and the stability and trend strength of the temperature time series) were found to be useful predictors of many streamflow features. The same applies to traditional attributes, such as the catchment mean elevation. Relationships between predictor and dependent variables were also revealed, while the spectral entropy, the seasonality strength and several autocorrelation features of the streamflow time series were found to be more regionalizable than others.

Signal decomposition and multiscale signal analysis provide many useful tools for time-frequency analysis. We proposed a random feature method for analyzing time-series data by constructing a sparse approximation to the spectrogram. The randomization is both in the time window locations and the frequency sampling, which lowers the overall sampling and computational cost. The sparsification of the spectrogram leads to a sharp separation between time-frequency clusters which makes it easier to identify intrinsic modes, and thus leads to a new data-driven mode decomposition. The applications include signal representation, outlier removal, and mode decomposition. On the benchmark tests, we show that our approach outperforms other state-of-the-art decomposition methods.

Structured and grounded representation of text is typically formalized by closed information extraction, the problem of extracting an exhaustive set of (subject, relation, object) triplets that are consistent with a predefined set of entities and relations from a knowledge base schema. Most existing works are pipelines prone to error accumulation, and all approaches are only applicable to unrealistically small numbers of entities and relations. We introduce GenIE (generative information extraction), the first end-to-end autoregressive formulation of closed information extraction. GenIE naturally exploits the language knowledge from the pre-trained transformer by autoregressively generating relations and entities in textual form. Thanks to a new bi-level constrained generation strategy, only triplets consistent with the predefined knowledge base schema are produced. Our experiments show that GenIE is state-of-the-art on closed information extraction, generalizes from fewer training data points than baselines, and scales to a previously unmanageable number of entities and relations. With this work, closed information extraction becomes practical in realistic scenarios, providing new opportunities for downstream tasks. Finally, this work paves the way towards a unified end-to-end approach to the core tasks of information extraction. Code, data and models available at https://github.com/epfl-dlab/GenIE.

Estimating information-theoretic quantities such as entropy and mutual information is central to many problems in statistics and machine learning, but challenging in high dimensions. This paper presents estimators of entropy via inference (EEVI), which deliver upper and lower bounds on many information quantities for arbitrary variables in a probabilistic generative model. These estimators use importance sampling with proposal distribution families that include amortized variational inference and sequential Monte Carlo, which can be tailored to the target model and used to squeeze true information values with high accuracy. We present several theoretical properties of EEVI and demonstrate scalability and efficacy on two problems from the medical domain: (i) in an expert system for diagnosing liver disorders, we rank medical tests according to how informative they are about latent diseases, given a pattern of observed symptoms and patient attributes; and (ii) in a differential equation model of carbohydrate metabolism, we find optimal times to take blood glucose measurements that maximize information about a diabetic patient's insulin sensitivity, given their meal and medication schedule.

Male and female heights both have a standard deviation of about 3 inches, with means of 70 inches and 64 inches. That’s a good first-pass model using round numbers. If you ask what the height of an average adult is, not specifying male or female, you get a mixture of two normal distributions. If we […] How flat is a normal mixture on top? first appeared on John D. Cook.  ( 2 min )

Thirty years it's taken me, A.I. that is not just as good as humans but better than humans at composing music: https://i.imgur.com/hReXJq1.png It passes the Turing Test, and it is also a revolution in the field of music in and of itself. In the meantime, no one has said anything nice to me in thirty years; just insults. I would feel dumb rewarding humanity with my creation; it would send the wrong message; it would affirm their bad behavior. Garbage species. Low IQ. submitted by /u/PussyFiller2022 [link] [comments]  ( 1 min )

I've been exploring the jax ecosystem and its many neural network libraries but I can't seem to settle on one. The main 5 which i am considering are Trax, Objax, Equinox, Flax, and Elegy, however I would like to hear which jax NN lib you use and why. submitted by /u/Southern-Trip-1102 [link] [comments]

I'm planning to create a clone of my own voice. Is there some kind of ideal sample text to record? I need 300 sentences. submitted by /u/headwar [link] [comments]

I am a researcher working on creating paths to indicate the primary and secondary neuronal connections in Corneal Confocal Microscopy images. The ground truth I have is images and sets of two-dimensional lines that indicate the primary and secondary paths as indicated in the image below (Ignore the green dots). The secondary and primary paths are always connected to each other. ​ https://preview.redd.it/r025qcpjddt81.jpg?width=834&format=pjpg&auto=webp&s=11a641988e0c6f8c1e17290fe9588f89ef530635 I am looking to find the most appropriate models to use for this task. The first thing that came to my mind was semantic segmentation. However, I am looking for other approaches that can be more suitable for drawing 1-pixel lines especially since the ground truth paths are indicated as 1-pixel-wide lines (1-pixel thickness) but the connections in the images have wider thicknesses. Any ideas for architecture or methods? submitted by /u/madr3z [link] [comments]  ( 2 min )

Source: https://www.baai.ac.cn/portal/article/index/cid/4/id/404.html Statement on the Alleged Plagiarism by “A Roadmap for Big Model” It has come to our attention that the survey report “A Roadmap for Big Model” uploaded on arXiv by a BAAI team is suspected of plagiarism. Immediately upon learning of the allegations, an internal investigation was organized to confirm the issue. BAAI is also initiating an independent review by third-party experts to further asses the issue and accountabilities. As a research institution that attaches great importance to academic standards, BAAI holds a zero-tolerance policy towards academic misconduct. We express our sincerest apologies to the authors of the original papers and to all of those affected. The report in question constitutes a collection …  ( 2 min )

Important note: Right now, the model only supports up sampling from any dimension to at most 256 pixels. I'll likely fix this restriction in the next few days. A few days back, I was searching for AI-based image up sampling models in for use within an offline JavaScript app. The latest approaches, such as SwinIR were unavailable for Javascript, so I just created a notebook that converts the SwinIR model from torch to TFJS in a relatively short kaggle kernel. I believe other transformer architectures can also be ported to JS like this. This is the link to the original paper of SwinIR. It requires around 1 GB of RAM to run. The size of model folder is 44 MB. It is quantized to float16. Anyway, hope someone will find this useful for their website or some other app. submitted by /u/Deep-Station-1746 [link] [comments]  ( 1 min )

Something that's always puzzled me is the ubiquitousness of 3x3 convolutions in computer vision. If I recall past discussion accurately, the main benefits of odd-sized kernels are that With the proper padding they maintain the width and height of their inputs, which makes it easier to think about/design neural network architectures. This is not possible with even kernels, unless you swallow the bullet and use asymmetric padding (which is rejected due to aesthetic reasons) Output pixels have a 1-to-1 mapping with input pixels (since odd-sized kernels have a proper "center"). This is considered a nice property -- perhaps (for instance) avoiding aliasing issues during segmentation tasks. Given these two points, we use 3x3 convolutions since they're the smallest odd-sized filter (exclud…  ( 6 min )

submitted by /u/MVTS_Ano [link] [comments]  ( 1 min )

What are open and/or interesting problems in modern reinforcement learning that can be tackled by the average PhD/PostDoc who doesn't have access to a massive compute cluster? The problem shouldn't need us to train our model for 10 months like OpenAI's Dota2 model. Please share your thoughts. submitted by /u/ginger_beer_m [link] [comments]  ( 1 min )

I've been helping a friend of mine learn about CV, but my knowledge starts getting spotty around 2017-2018. In this spirit I'm hoping to discuss the biggest developments in CV in the last 5 years. I know that ViTs have been developed in that time but I'm hoping to fill in my knowledge gaps! A list of topics, important papers, big ideas, or anything else is much appreciated! ​ Edit: Thank you for the discussion everyone 🙌! I've been in and out of meetings but am reading through all responses now submitted by /u/SleekEagle [link] [comments]  ( 3 min )

Hi, I’m a machine learning platform engineer. I’ve been using, exploring and developing model deployment tools and platform for several years. Very often, I found that many of the tools or managed service of AI platform, are not very welcome by many users. Some think these tools are unnecessarily complicated. I'm currently developing a library in my free time trying to fill the gap. And I also want the library to get well integrated with most users' deployment environments. Would you like to share how and where do you serve your model? Using kubernetes? Self developed or existing tools? Thanks～ P.S. If you are interested, you can visit my project to submit an issue/PR or join the discussions, welcome to help: Pinferencia View Poll submitted by /u/Remote_Cancel_7977 [link] [comments]  ( 1 min )

Hi guys! I recently created & published a dataset of YouTube video thumbnails on Kaggle (YouTube Thumbnail Dataset), I've tried to make the dataset as diverse as possible, It contains thumbnails from all varieties of YouTube channels. This dataset goes hand in hand with another dataset (containing YouTube video annotations) that I created, namely YouTubers Saying Things. The dataset contains 91 unique YouTube channels, and 10 categories, these categories are assigned by me manually to these channels. (Comedy, Science, Automobile, VideoGames, Food, Entertainment, Informative, Blog, News, Tech) All kinds of feedback and criticism are welcome, and also if you guys want some particular channel to be included in both these datasets, feel free to comment on this post, or raise an issue on the Github repositories for both these datasets, I will surely add them in the next version. Links to the datasets: YouTubers Saying Things Kaggle, Github YouTube Thumbnail Dataset Kaggle, Github submitted by /u/alcatraz2217 [link] [comments]  ( 1 min )

Hi, I am doing researches about transfer learning for XGboost. I am currently working with a small dataset from a company in Spain (short history) and the scoring is poor. I have worked before with the same company in France and the scoring was great as I had plenty of data thanks to a big history. How could I improve my score with the data from Spain with the help of data from France ? Could I use transfer learning, or data mutualization, or data augmentation ? If anyone has faced before a similar problem, or has read some papers about it, I would love to hear about it. Thank you! submitted by /u/Cutset [link] [comments]  ( 1 min )

submitted by /u/hardmaru [link] [comments]

Hey all, Im trying to get up to date with Deep Learning literature, so the last week I was going through CNNs. Here's a general view of what Ive learned so far. Large filters suck, you can get better accuracy with smaller filters and more non linearities Depth is the most important for CNNs over width or filter sizes. ReLU activation is generally better, as Sigmoid/tanh gradients tend to fall off towards the ends Convolution layers are only translation invariant. Stacking multiple features together and passing them through MaxPool helps rotational invariance and scaling although not completely Residual connection help address vanishing gradient and help improve the overall training procedure Inception models worked well, as they mixed different filter sizes together helping the model learn diverse features Most current work is with Transformers, although Im not sure why. ConvNext shows similar performance can be achieved through large CNNs Do add to this if I missed anything, or if there's anything you don't know about submitted by /u/Bibbidi_Babbidi_Boo [link] [comments]  ( 5 min )

The authors of Make-A-Scene propose a novel text-to-image method that leverages the information from an additional input condition called a “scene” in the form of segmentation tokens to improve the quality of generated images and enable scene editing, out-of-distribution prompts, and text-editing of anchor scenes. As for the details, let’s dive in, shall we? Full summary: https://t.me/casual_gan/284 Blog post: https://www.casualganpapers.com/text-to-image-vqvae-scene-generation/Make-A-Scene-explained.html Make-A-Scene arxiv / code (by Casual GAN Papers Community) Join the discord community and follow on Twitter for weekly AI paper summaries! submitted by /u/KirillTheMunchKing [link] [comments]  ( 1 min )

I’m sure we all remember the story of “The Little Engine That Could.” A little railroad engine was built for pulling a few cars on and off the switches. When more powerful engines are asked to pull a load over a steep hill, they respond “I can’t; that is too much a pull for me”.… Read More »Fallacy of Becoming Data-driven – Part 1: Becoming Value-obsessed The post Fallacy of Becoming Data-driven – Part 1: Becoming Value-obsessed appeared first on Data Science Central.  ( 6 min )

You can establish feature stores to provide a central repository for machine learning (ML) features that can be shared with data science teams across your organization for training, batch scoring, and real-time inference. Data science teams can reuse features stored in the central repository, avoiding the need to reengineer feature pipelines for different projects and […]  ( 10 min )

Amazon Lex can add powerful automation to contact center solutions, so you can enable self-service via interactive voice response (IVR) interactions or route calls to the appropriate agent based on caller input. These capabilities can increase customer satisfaction by streamlining the user experience, and improve containment rates in the contact center. In both the self-service […]  ( 6 min )

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3D landscape modeling has seen a rise in its popularity and applications in recent years. It has countless applications in the fields of civil engineering, earth sciences, military applications, and many others. Geometric 3D models are typically developed using the city geography markup language (CityGML), and the Level-of-Detail (LoD) building model is the preferred model for building 3D models using CityGML. The use of Satellite imagery for landscape modeling provides the advantage of covering a wide area and is low cost. However, developing LoD2 models using satellite imagery remains a big challenge. Building models in such a way involves complex steps demanding heuristics-based approaches and ML-based detection paradigms. In a recent paper, researchers at the Ohio State University propose a SAT2LoD2 to facilitate the development of 3D landscape models. SAT2LoD2 is an open-source, python-based GUI-enabled software that takes the satellite images as inputs and returns LoD2 building models as outputs. The software also has the feature of taking road networks and custom maps as additional inputs for better results. Continue Reading Paper: https://arxiv.org/pdf/2204.04139v1.pdf Github: https://github.com/gdaosu/lod2buildingmodel submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

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https://youtu.be/0x0to1wNh6s A new enterprise project model supported by AI. In the near future, the growing introduction of automation and artificial intelligence will require the updating of most of the activities in the production world, along with changes to contracts, tasks, and integration processes between man and machine. This is supported by the Accenture "IT's Learning" study, according to which 81% of jobs will suffer the impact of AI and robotization. submitted by /u/neologos52 [link] [comments]  ( 1 min )

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What does 2022 look like for AI? Let's find out. https://us.sganalytics.com/blog/top-ethical-challenges-in-ai-the-price-of-progress/ submitted by /u/JencyJane [link] [comments]

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The authors of Make-A-Scene propose a novel text-to-image method that leverages the information from an additional input condition called a “scene” in the form of segmentation tokens to improve the quality of generated images and enable scene editing, out-of-distribution prompts, and text-editing of anchor scenes. As for the details, let’s dive in, shall we? Full summary: https://t.me/casual_gan/284 Blog post: https://www.casualganpapers.com/text-to-image-vqvae-scene-generation/Make-A-Scene-explained.html Make-A-Scene arxiv / code (by Casual GAN Papers Community) Join the discord community and follow on Twitter for weekly AI paper summaries! submitted by /u/KirillTheMunchKing [link] [comments]  ( 1 min )

Goodhart’s law famously says: “When a measure becomes a target, it ceases to be a good measure.” Although originally from economics, it’s something we have to grapple with at OpenAI when figuring out how to optimize objectives that are difficult or costly to measure.  ( 4 min )

New program strives to bridge the talent gap for underrepresented groups in the tech industry.  ( 5 min )

Perovskite materials would be superior to silicon in PV cells, but manufacturing such cells at scale is a huge hurdle. Machine learning can help.  ( 7 min )

Posted by Jeffrey Zhao and Raghav Gupta, Software Engineers, Google Research Modern conversational agents need to integrate with an ever-increasing number of services to perform a wide variety of tasks, from booking flights and finding restaurants, to playing music and telling jokes. Adding this functionality can be difficult — for each new task, one needs to collect new data and retrain the models that power the conversational agent. This is because most task-oriented dialogue (TOD) models are trained on a single task-specific ontology. An ontology is generally represented as a list of possible user intents (e.g., if the user wants to book a flight, if the user wants to play some music, etc.) and possible parameter slots to extract from the conversation (e.g., the date of the flight, the…  ( 8 min )

A few days ago I wrote about the Hilbert transform and gave as an example that the Hilbert transform of sine is cosine. We’ll bootstrap that example to find the Hilbert transform of any periodic function from its Fourier series. The Hilbert transform of a function f(t) is a function fH(x) defined by where the […] Hilbert transform and Fourier series first appeared on John D. Cook.  ( 2 min )

I got an evaluation copy of The Best Writing on Mathematics 2021 yesterday. One article jumped out as I was skimming the table of contents: A Zeroth Power Is Often a Logarithm Yearning to Be Free by Sanjoy Mahajan. Great title. There are quite a few theorems involving powers that have an exceptional case that […] Logarithms yearning to be free first appeared on John D. Cook.  ( 2 min )

​ https://preview.redd.it/qtgp7dzx2bt81.png?width=850&format=png&auto=webp&s=ccf391e8a1613d5405c137296bdf853010fc3f19 Not speaking specifically about autoencoders here, but about general neural networks. As I understand correctly, "latent space" refers to one of the fully connected layers of the network and the dimensionality of the space is equal to the number of the neurons in this layer. This would mean, that each of the layers has a different "latent space" representation of the learned data distribution. Do I understand it correctly? I got really confused because people seem to sometimes refer to latent space as to all of the possible activations of all of the neurons in the network (each neuron of the network is one dimension of a latent space) OR EVEN to all of the PARAMETERS of the network (each parameter is one dimension of the latent space (??)). Do we have a separate name for these? How do we call the parameter space of a neural network? Is my original intuition even correct? submitted by /u/bzqp2 [link] [comments]  ( 1 min )

In this research, the researchers from the paper ‘ LilNetX: Lightweight Networks with EXtreme Model Compression and Structured Sparsification’ talk about the importance of larger parameter-heavy and computationally costly architectures in deep neural networks (DNNs) and how it improves the computer vision tasks. They also mentioned in the paper that it is not as simple as it seems since, as the DNNs become more common in the business, they are frequently required to be trained multiple times, communicated across the network to various devices, and executed under hardware limits with minimum loss of accuracy, all while maintaining accuracy. Then the question arises of how to reduce the models’ size on the devices while still enhancing their run-time. Explorations in this field have tended to take one of two paths: lowering model size via compression approaches or reducing computing demands through model pruning. The main achievement of this research from the University of Maryland and Google Research is the introduction of ‘LilNetX’, an end-to-end trainable neural network technique that allows learning models with specified accuracy-rate-computation trade-offs. Prior work has taken a piecemeal approach to these difficulties, which necessitates post-processing or multistage training, which is not efficient and does not scale well for big datasets or architectures. To encourage modest model size, the strategy is to create a joint training goal that penalizes the self-information of network parameters in a reparameterized latent space while simultaneously incorporating priors to increase structured sparsity in the parameter space to decrease computation. Continue Reading Paper: https://arxiv.org/pdf/2204.02965.pdf Github: https://github.com/Sharath-girish/LilNetX submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

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If i want to take the partial derivative of the error with respect to a certain weight, it would be similar to taking the derivative of say y = value * weight + bias but if i hold the value and bias still, the derivative just becomes the value of the weight, like how the derivative of y = 3x is just 3… so what do I do? it doesn’t make sense to multiply 3 by a learning variable and make that the new weight, so what am I missing? submitted by /u/-i-hate-this-place- [link] [comments]  ( 2 min )

I'm trying to train a seq2seq model that generates a sentence with T words using reinforcement learning. The input and all the previously generated words form the state of the environment, and generating a word in the sentence is considered an action. In the previous methods [1, 2], the immediate reward r(s_t, a_t, s_{t+1}) for the t-th action a_t is 0 when t < T, and the reward is the CIDEr score (a scalar that measures the quality of the sentence) of the entire sentence when t = T. The policy is updated after the entire sentence is generated. I designed a new reward for each action, and the new reward for the t-th action is not zero when t < T. However, the reward of each action can only be calculated when the entire sentence is generated since it relies on the CIDEr score of the entire sentence, i.e. the reward for all the actions relies on the final state s_T. Can I still define the reward in the form of r(s_t, a_t, s_{t+1}) ? [1] Rennie et al. Self-critical sequence training for image captioning, CVPR 2017: 7008-7024. [2] Ranzato et al. Sequence level training with recurrent neural networks, ICLR 2016. submitted by /u/entalent [link] [comments]  ( 1 min )

I am reading that paper A Distributional Perspective on Reinforcement Learning, and it is related to measure theory. Is it worth to spend time to study whole real analysis and measure theory? submitted by /u/Professional_Card176 [link] [comments]  ( 1 min )

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Hi all, I am creating a multiple-goal environment. Which is an 8*8 discrete map with a start and terminal state (only one) change after each episode. The reward is 100 for reaching the terminal state and -1 for the rest. In fact, I am not sure if the reward is reasonable. I used PPO from SB3 and I can easily finish it. But when I go offline, using HER+DQN, the training is very bad. Feel free to run it here or take a look at the env and training result. Thank you so much! https://colab.research.google.com/drive/1Mt5Yje7GTyjOBHL09zC9C1L05xpTAK9v?usp=sharing submitted by /u/AnimatorRemarkable20 [link] [comments]  ( 1 min )

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Hi All, I am working on a MARL fintech project where I use DDQN and for Q-value, I use LSTM bacause it is time series data. This is a project overview. It has 7 features which is derivatives of ask and bid price and has 12 action spaces for action taking. Is it possible to generate a good reliable model using only 7 features for 12 action spaces? Number of feature or quality of feature is important for taking good decision in RL. Open for Suggestion #Reinforcement_Learning #MARL submitted by /u/laxuu [link] [comments]  ( 1 min )

Hi, these day I really interested in self-supervised RL. Especially only based on state novelty. So I wanted to re-implement APT(Behavior From the Void: Unsupervised Active Pre-Training). but my re-implementation showed not meaningful behaviors compared to official implementation. official implementation uses drq-v2 and intrinsic curiosity module. So, I want to re-implement APT as described in paper(using drq-v1 and contrastive learning). Is there someone to check my reimplementation? https://github.com/seolhokim/apt In that repository, DrQ-v1 works well, but only apt doesn't work! I can't understand why agent stop moving when pre-training. ​ Really thank you for reading. submitted by /u/Spiritual_Fig3632 [link] [comments]  ( 1 min )

In the past few years, high-quality automated text-to-speech synthesis has effectively become a commodity, with easy access to cloud-based… Continue reading on Becoming Human: Artificial Intelligence Magazine »  ( 15 min )

In deep learning and machine learning, having a large enough dataset is key to training a system and getting it to produce results. So what does a ML researcher do when there just isn’t enough publicly accessible data? Enter the MLCommons Association, a global engineering consortium with the aim of making ML better for everyone. Read article > The post MLCommons’ David Kanter, NVIDIA’s Daniel Galvez on Improving AI with Publicly Accessible Datasets appeared first on NVIDIA Blog.  ( 2 min )

Episode 135 | April 13, 2022 In “Just Tech: Centering Community-Driven Innovation at the Margins,” Senior Principal Researcher Mary L. Gray explores how technology and community intertwine and the role technology can play in supporting community-driven innovation and community-based organizations. Dr. Gray and her team are working to bring computer science, engineering, social science, and […] The post Just Tech: Centering Community-Driven Innovation at the Margins Episode 3 with Dr. Sasha Costanza-Chock appeared first on Microsoft Research.  ( 31 min )

IoT and Machine Learning are the most advanced and evolving technologies that continue to rise in today’s modern world, simplifying human efforts and making lives easier. These technologies have proved to streamline operations and workflows for various industries and provide more robust and scalable applications that allow users to make things done seamlessly.  In recent… Read More »How IoT Uses Machine Learning To Change The World The post How IoT Uses Machine Learning To Change The World appeared first on Data Science Central.  ( 6 min )

Recent advances in machine learning (ML) and artificial intelligence (AI) technologies are helping enterprises across industries quickly move from their use cases from the pilot stage to production and operationalization. According to a report by McKinsey & Company, by 2030, businesses that fully absorb AI could double their cash flow, while companies that don’t could… Read More »Drag-and-drop Data Pipelining: The Next Disruptor in ML The post Drag-and-drop Data Pipelining: The Next Disruptor in ML appeared first on Data Science Central.  ( 3 min )

As the Internet of Things (IoT) is gradually moving from being a centralized structure to a more complex network of innumerable decentralized smart devices, the need for security of data will be acknowledged to a greater degree, thereby promoting the expansion of the global IoT security market. The larger the volume of the data transferred… Read More »Advances Highlight the Future of IoT Security The post Advances Highlight the Future of IoT Security appeared first on Data Science Central.  ( 3 min )

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I'm trying to make a model to play a car racing simulator. I have a dataset with the inputs used(human) to get fast lap times. I would like to make a model that reads the game video output and predicts the arrow key inputs to get a fast lap time. It seems, to me, that a CNN with encoder-decoder layers trained on the keyboard inputs would work. Is this a good architecture? I'm also having a hard time finding useful literature. please let me know if there is anything I should look into or do differently. submitted by /u/newroadkill [link] [comments]  ( 1 min )

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Is it good or bad? Also, I was wondering what art goes big as an NFT? submitted by /u/Ok-Passion-6574 [link] [comments]

In recent years, the rise of Deep Learning has continuously brought innovations to many fields, and the medical domain is one of them. AI applications in this field are countless: from pre-operative diagnosis to disease classification, from skill assessment to post-operative rehabilitation. Among them, systems to assess surgical skills and provide feedback to improve technique could help in decreasing the number of complications in surgical procedures, which are still the third leading cause of death globally. AI can be an additional coach for surgical trainees and an expert colleague for experienced surgeons. But, to train an AI system, reliable data are fundamental. The more utilized type of data in this context is undoubtedly video streams, as a camera is less invasive than other types of sensors, such as ArmBand or EEG, which could weigh on the surgeon’s performance given their physical bulk. This applies particularly to laparoscopic surgery, where an in-body fiber-optic camera is used to visualize the operating area and facilitate rapid data collection. For this reason, the majority of computer-assisted systems focus on laparoscopic surgery. Continue Reading Paper: https://arxiv.org/pdf/2112.07219.pdf https://preview.redd.it/84mwdw81l4t81.png?width=741&format=png&auto=webp&s=636aff067560876d14f37caccb83bd951e991c68 submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

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Roughly five years ago, I created this thread where I outlined my realization about the imminency of synthetic media. This was before transformers blew up, before StyleGAN, before GPT-2, when WaveNet and DeepDream were still among the best we could do, and when predictive text algorithms that were barely better than Markov Chains were still the state of the art. In five short years, the state of artificial intelligence has changed overwhelmingly, to the point it's barely recognizable. Looking back to 2017, I now get this sense of everything feeling so primitive and fake. I've stated many times that AI before roughly 2019 was a bunch of digital magic tricks, and the field as a whole was essentially a giant Potemkin village that utilized clever sleight of hand and advertising to make it se…  ( 7 min )

BAAI recently released a two hundred page position paper about large transformer models which contains sections that are plagiarized by over a dozen other papers. In a massive fit of irony, this was found by Nicholas Carlini, a research who (among other things) is famous for studying how language models copy outputs from their training data. Read the blog post here submitted by /u/StellaAthena [link] [comments]  ( 2 min )

I've been trying to find information about the subject, but almost every result is TTS, and the only example of what I actually want (Respeecher) costs 2 grand a year. Are there any (preferably open-source) other alternatives? submitted by /u/UncertainOutcome [link] [comments]  ( 1 min )

I understand that workshop quality is dependent more on the workshop itself rather than the host conference. But, in general, how are workshops from CVPR, NeurIPS, ICLR, ICML, etc. viewed by the community in relation to one another? submitted by /u/avd4292 [link] [comments]  ( 1 min )

Hi everyone! I'm really happy to finally publish the work I've been doing on the Cellular Automata called Lenia. It is a JAX library called Leniax and allows one to simulate thousands of simulations in parallel using CPU, GPU, or TPU. With it, you can: Simulate Conway's Game of Life Simulate multiple Lenia simulations in parallel Use gradient descent to search for Continuous CA parameters Launch a QD search to discover a ton of diversity in Lenia. Check out the blog post for some visual results The main goal of this work was to advance the state of automatic discovery for those systems. 10 months ago, I bet on QD to do so, turns out it indeed works! QD algorithms really rock! The code is completely open-source with all the examples, notebooks, and even experiments I ran. (See the doc for more links) I would love to have feedback on this and of course, if you find that subject interesting, engage with our community! Cheers! submitted by /u/morgangiraud [link] [comments]  ( 2 min )

So I am doing some object detection on Pavement Defects. I've already collected the data with some annotations but the model is performing rather poorly. For example, the `maP` is about `0.12` for the whole data. By examining the data, I think one of the reasons is that some of the defects such as cracks, or faded pavement markings are not so clear and either casted by a shadow or too bright from the sun. Image Example #1 Or from motion blur Image Example #2 Is there any image preprocessing technique aside maybe from CLAHE that could be applied? Moreover, I am currently using YOLOv5 for this. submitted by /u/sarmientoj24 [link] [comments]  ( 3 min )

The copent package implements the method for estimating copula entropy (mutual information) and transfer entropy (conditional mutual information / conditional independence). This version add a new feature (an argument 'mode') for dealing with large data when memory is limited. Github: https://github.com/majianthu/pycopent PyPI: https://pypi.org/project/copent/ any comments are welcome. submitted by /u/majianthu [link] [comments]  ( 1 min )

Hey everyone! At ZenML, we released today an integration that allows users to train and deploy models from pipelines in a simple way. I wanted to ask the community here whether the example we showcased makes sense in a real-world setting: Context ZenML is an extensible, open-source MLOps framework to create production-ready machine learning pipelines. Built for data scientists, it has a simple, flexible syntax, is cloud- and tool-agnostic, and has interfaces/abstractions that are catered towards ML workflows. Seldon Core is a production grade open source model serving platform. It packs a wide range of features built around deploying models to REST/GRPC microservices that include monitoring and logging, model explainers, outlier detectors and various continuous deployment strategies such…  ( 2 min )

I am working in the retail domain atm, and train a lot of image classifiers. I have always used imagenet as pretrained to train my model upon. I thought it would be straightforward to train a backbone on a big retail dataset(1000+ classes), and then use that as pretrained and it'll reduce the time it takes for my models to generalize. Turns out, the model took more epochs to train when using the retail backbone, then the imagenet one. Isn't this counter-intuitive? What else can I do to make by backbone better? submitted by /u/lMAObigZEDONG [link] [comments]  ( 1 min )

Hi, I have a fairly interesting project that I am working on. I have a model that has some samples which are completely unpredictable, random noise, and some that are reliably predictable. How would you go about separating out the samples which can be predicted, identifying them going forward, and retraining on a cleaned dataset with only those samples? Interested to see someone else's approach to this. Edit: I forgot to mention that my data is from an embedding matrix from ordinal categorical features. submitted by /u/Katapilla_Killa [link] [comments]  ( 2 min )

There is the original paper and there was a subsequent paper by other authors titled: On the Convergence Theory of Debiased Model-Agnostic Meta_Reinforcement Learning I've been working on implementing the latter paper on the HalfCheetah environment. However, my attempts have been unsuccessful so far (I know the authors provided the code, but I am trying to write my own code to check my understanding). I'd like to know any tips/tricks that anyone can share and just to know about people's experiences, especially using MAML for RL. submitted by /u/carlml [link] [comments]  ( 1 min )

I'm always having these ideas for projects and papers, then I look around a bit, and I find someone that has already studied that idea and published it. It's genuinely annoying, It's been 6 months now, and all the papers are newly published (2021 mostly) so It's even more annoying. How do you deal with that ? and How do you find a niche that no one is touching. I just started a PhD, so It's really stressing me out. I feel like I'll never be able to advance on my thesis, and that I should just quit, because better work has already been done. submitted by /u/AlanRoofies [link] [comments]  ( 4 min )

Hi there, in the case if any of you use the openCV BFMatcher with NORM_L2, you can try to use my recent pet project: https://github.com/kmkolasinski/fast-bfmatcher Basically the speed-up is achieved by using faster replacement for BLAS, a BLIS library and some custom implementations written in C and cython. submitted by /u/kmkolasinski [link] [comments]  ( 1 min )

My current research heavily involves generative vision transformers and after some experimentation it seems like the choice of a LR scheduler is a crucial factor for proper convergence. Does anyone know of any comparison studies done recently that explore various types of schedulers for generative tasks? submitted by /u/Megixist [link] [comments]  ( 4 min )

With the advent of deep learning models, automated data extraction is becoming more accessible. In this article, we demonstrate step-by-step how to fine-tune layoutLM V2 on invoices starting from data annotation to model training and inference. Enjoy the read and if you have any questions, leave them below. submitted by /u/UBIAI [link] [comments]  ( 1 min )

Posted by Yingwei Li, Student Researcher, Google Cloud and Adams Wei Yu, Research Scientist, Google Research, Brain Team LiDAR and visual cameras are two types of complementary sensors used for 3D object detection in autonomous vehicles and robots. LiDAR, which is a remote sensing technique that uses light in the form of a pulsed laser to measure ranges, provides low-resolution shape and depth information, while cameras provide high-resolution shape and texture information. While the features captured by LiDAR and cameras should be merged together to provide optimal 3D object detection, it turns out that most state-of-the-art 3D object detectors use LiDAR as the only input. The main reason is that to develop robust 3D object detection models, most methods need to augment and transform th…  ( 8 min )

(A Sci-Fi Ultrashort)  ( 5 min )

The programs are designed to foster an understanding of how artificial intelligence technologies work, including their social implications.  ( 8 min )

In your opinion, what is the best gridworld environment? I want to compare different RL algorithms on it. I’m looking for something super basic: - start and goal state - some obstacles - customisable: move the start and goal state, place obstacles in different points, modify reward map etc. - computationally efficient Thank you submitted by /u/wiston_smith [link] [comments]  ( 1 min )

Hi all, I've built a custom gym env and am using Stable Baselines3 to train an agent. I would like to visualise in TensorBoard the maximum reward achieved for each episode. I have these values in a list in my env, and I am trying to create a custom Callback to plot this in TensorBoard but it's not working. I've looked over the documentation and other forums but can't figure out how to do this. Can anyone help me out? 🙏🏽 Thank you! submitted by /u/leozinho2r [link] [comments]  ( 1 min )

Recently, we have released the source code of our winning solution for the NetHack 2021 NeurIPS Challenge: https://github.com/maciej-sypetkowski/autoascend We hope that it will help in leveraging this complex environment, that still seems to be beyond capabilities of reinforcement learning. Check out links in the README "Description" section for more context. submitted by /u/procedural_only [link] [comments]  ( 1 min )

Do any of u have any good rl project suggestion or a complete project for college major , I have only done work on some self playing Atari , mario games , if u have any good idea please suggest 🙌 submitted by /u/stoned_egineer [link] [comments]  ( 1 min )

Does anyone have experience training agents to play platformer games like mario? I am trying to train an agent for the platformer game Jump King to get past atleast a few levels, using DQN but the agent is performing poorly after 8000 episodes of training, (one episode being the agent spawns at the start and has 15 seconds or so to jump around gaining reward) he is barely able to get past the first level most of the time :c I am using a very basic sequential network of 2 Linear layers with inputDim 4, outputDim 4, and hiddenDim 32. and because my state is not using any image data, its just (current_level, x_pos, y_pos, jumpCount) as input to the network . As for the reward, I am using the y position to give reward if the agent is getting to a new level (large reward) or making progress in the current level (curr_y > old_y), otherwise the agent gets a negative reward. Should I consider using a CNN and image data to train this agent like in the atari games paper, or is using image data and a conv net going to perform worse rather than using my current state? Should I consider combining image data with the current state, or just keeping the current non-image data state but ? Also, roughly how long should I be training the agent for? is 8000 episodes not enough? 1 episode takes roughly 7 seconds in time (it is using pygame engine and I turned off the rendering and I think that made it a little bit faster) This is my first time training an agent for a hard game like this using DQN so I would appreciate any tips/advice to improve the agent! repo: https://github.com/senweim/JumpKingAtHome submitted by /u/TernaryJimbo [link] [comments]  ( 2 min )

I want to hear about your impressive environment! Specifically, I want to make my custom environment well using various library like openAI gym. In this contxet, I find out the highway-env https://github.com/eleurent/highway-env/ ! I think this environment has convinient API for users. Thus, I make my custom env with referencing the highway-env https://preview.redd.it/ndl1h1dvpzs81.png?width=711&format=png&auto=webp&s=267320a9713d98e7e49c4bb89423e5a9612bad8e In this line, could you speak your best environment? It doesn't matter about your best env has any advantage! submitted by /u/Seungeon94 [link] [comments]  ( 1 min )

Python is a general-purpose computation language, but it is very welcomed in scientific computing. It can replace R and Matlab […] The post Scientific Functions in NumPy and SciPy appeared first on Machine Learning Mastery.  ( 12 min )

Hey guys, I tried building a PPO model for Wordle. My initial test was checking the performance of the model with just 100 words. The agent was able to learn within a few thousand epochs and had an average guess length of about 2.8 before it could correctly identify the words. However, when i extend the action space to the entire 2.3k words, the model barely learns. Even after a few 100k iterations, the mean length revolves around 5.9 (given wordle has a max of 6 attempts per game) Any suggestions on how to help the agent learn faster in large action spaces? ​ I also tried an embedding based approach, but the performance was very similar. ​ Thanks submitted by /u/altair9335 [link] [comments]  ( 1 min )

Has anyone managed to get a consistent score of > 9 on CoinRun? I understand that some generated levels require LSTMs in order to be solvable 100% of the time, but even excluding these hard-core levels I can see some occasions where my agents are not operating with 100% effectiveness. For some reason "fully solving" CoinRun seems harder than expected. The papers on CoinRun usually just show the results after 100mm steps or so, but I am more interested in what the community has achieved with "normal setups". submitted by /u/tmuxed [link] [comments]  ( 1 min )

I trained RL agent using stable baseline library and gym env. When I am trying to test agent, this makes different action when I am re running again. I used the same seed in test env. for i in range(length-lags-1): action, _states = model.predict(obs_test) obs_test, rewards, dones, info = env_test When I am runnig again the above code, I am getting the different results. submitted by /u/Mariam_Dundua [link] [comments]  ( 1 min )

Hi all, I'm trying to teach my custom fbx model to walk with the help of ppo, as in the example from ml agents. I have difficulties with the exact import and the assignment of rigidbody here, that is, the neural network is being trained, but for some reason physics does not work. Has anyone seen it, or does anyone have an example of how to train a unity custom fbx model using ml agents? Thx all! submitted by /u/IndependenceCivil576 [link] [comments]  ( 1 min )

I want to run RL algorithm on Apache Spark. However, RL does not exists in Spark's MLib. Is it possible to implement it? any links may help. Thank you in advance submitted by /u/fatenLouati [link] [comments]  ( 1 min )

We will introduce the general process of self-driving tasks first and then the development of Reinforcement Learning in self-driving cars. The general process of self-driving tasks includes perceiving, decision-making, planning and controlling. The tasks of perceiving have adopted deep learning and that did a good job. Being different from monitoring learning, decision intelligence AI methods, which are represented by reinforcement learning, model the environment as Markov Decision Process(MDP)to get optimization. In sequential decision problems the utility of agent's actions do not depend on single decisions, expressed with the state, which the agent would have gotten into, as the result of this decision, but rather on the whole sequence of agent's action. Here, one thing that needs t…  ( 4 min )

Paper: https://arxiv.org/abs/2112.04035 Yes, the paper is cautious about comparing the model one-to-one to the brain “Note, we are not saying the brain is closely related to transformers because it learns the same neural representations, instead we are saying the relationship is close because we have shown a mathematical relationship between transformers and carefully formulated neuroscience models of the hippocampal formation.” While objections like "its just correlation/relation, its not exactly the same!!" are true to an extend, its still a very unexpected observation that, they're even remotely similar. Needless to say, Transformers were not inspired from the brain - and as more evidence collates (https://www.nature.com/articles/s42003-022-03036-1 --> Activations are linearly correlatable) it does feel mysterious; perhaps atleast some of the systems used by the brain converge on an efficient pattern discovered by our backpropogated friends... [insert 'coincidence? I think not!' meme] submitted by /u/Competitive-Rub-1958 [link] [comments]  ( 1 min )

I'm looking for a generative-LM equivalent of an EfficientNet-Lite, for inference on devices with limited to no VRAM. I know about some popular ones like DistilGPT2. But it's been 2 years after its release. Surely, someone improved their size/performance ratio, right... right? Thank you for your time. 🤗 submitted by /u/Deep-Station-1746 [link] [comments]  ( 1 min )

This is just for fun, not to be taken too seriously. But I'm curious what are the reputations among the community for various research divisions (specifically AIML) of major companies, ie: Google, Facebook/Meta, Microsoft, Amazon, NVIDIA, IBM, etc. My perceived (albeit naive) view is Google > Facebook > MSR are top tier. Don't know much about the others. But I've read that some people consider MSR most prestigious due to their academic environment. But I've seen that Google and FB dominate in terms of major publications, ie: vision transformers are associated with Google. submitted by /u/avd4292 [link] [comments]  ( 2 min )

Hi all, Today we open-sourced Squirrel, a data infrastructure library that my colleagues and I have been working on over the past 1.5 years: https://github.com/merantix-momentum/squirrel-core We’re a team of ~30 ML engineers developing machine learning solutions for industry and research. Across all our projects, we need to load large-scale data in a fast and cost-efficient way, while keeping the flexibility to work with any possible dataset, loaded from local storage, remote data buckets or via APIs such as HuggingFace. Not finding what we were looking for, we decided to build it ourselves. Squirrel has already proven its value in our deep learning projects at Merantix Momentum and shows competitive benchmark results (check them out here). We’re super excited to share it with the OSS community and hope that you can benefit from it as well! Looking forward to hearing your feedback and questions :) submitted by /u/Nextpenade [link] [comments]  ( 1 min )

I want to apply an already trained ML model on a huge textual data set. I have funds to rent Cloud GPUs, but have not much experience using them. Preferably, I do the setting up of the environment only once (downloading of the data, model, software packages, etc) only once and then simply send ~100-200 scripts each to their own GPU for processing. Then at the end everything is in the same location and I can easily send back the final result file (~100-200 output files concatenated together) back to my PC. Any advice on how to do that? All GPU renting servers only have 1-8 GPUs per server and do not (seem) to allow for sharing of the environment, which seems very inefficient to me. All comments are appreciated. submitted by /u/Intelligent-End2673 [link] [comments]  ( 2 min )

Hi, was wondering if anyone else has looked into "A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent" and in particular eqn. 2, which is the derivation of Section 3.5 of "Train faster, generalize better: Stability of stochastic gradient descent" adapted for the case where the underlying loss we are interested in guaranteeing generalisation for is upper bounded by M (rather than 1 as assumed by Hardt et al). In the case of minibatch SGD, the number of datapoints n becomes the number of minibatches, as ideally one would like to reduce the number of steps T by maximizing the learning rate, which requires maximizing the minibatch size for the loss to actually converge to 0 on the training data. However, what I am unsure about is, specifically for the classification task where we typically minimize the cross-entropy objective, whether the cross-entropy objective is an upper bound on any kind of M-bounded loss function. In the ideal world, I would like to show that it upper bounds the 0-1 loss which means the cross-entropy over the dataset is an upper bound on the classification accuracy and any generalization statement automatically becomes a statement about the very practical metric of accuracy. Such a statement about cross-entropy upper-bounding 0-1 is made in Section 3C of "Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization". However, one can provide a counterexample in the limit of the softmax "temperature" parameter where the predicted class distribution becomes uniform, in the case of 2 classes, for the typical case of log being the natural logarithm (it is no longer a counter-example if log base 2 is used). I haven't been able to show or find proof that this statement "xent >= 0-1" is true (for some logarithm base and some number of classes) and was hoping that someone might have. submitted by /u/wakeupandshave [link] [comments]  ( 1 min )

Since going open source in March 2020, MindSpore gone from strength to strength. The deep learning framework has been downloaded by over 1.2 million users; algorithms running on MindSpore have been published in AI journals or presented at conferences; and countless developments have been released in device-edge-cloud scenarios to transform business fields, such as intelligent manufacturing, cloud, wireless, data communication, energy, and consumer business. Built on extensive experience from the scientific, academic, and industrial sectors, MindSpore-based AI papers accounted for 11% of all AI papers in October 2021, ranking No.2 worldwide by month, and No.3 worldwide in Q4 2021. In this blog post, based on a paper published in ICCV 2021 by Professor Mang Ye of Wuhan University, we intro…  ( 3 min )

Hi all, I've been working on reinforcement learning lately, but wanted to come to the general ML subreddit to seek inspiration from other disciplines. I've been working on strategies to decrease the training time for my real-world inverted pendulum experiment. Specifically, I am trying to pre-train the Q network in a simulation before deploying. The strategy that I have found most successful right now is this: start with randomly generated weights REPEAT OF AN EPOCH: - Load new_weights to Q Network - initialize an environment with randomly generated parameters (i.e. random mass, lengths, etc). - Train agent on environment for 100 episodes - Save new_weights I have tried a variety of strategies to add a little bit more control over this process. I've tried a soft update that never showed improvement. W = old_weights * (1 - alpha) + new_weights * alpha I have tried an additive update which was slightly successful. Measured the success of each network as the sum of rewards over the epoch. A = (old_R)/(old_R+new_R) ; B = (new_R)/(old_R+new_R) W = old_weights * A + new_weights * B But none of these work as well as just using the most recent weights. I've included some results if anyone's interested. The first graph is three test trials with random initial weights, the second graph is with pre-trained weights. This is a pretty hand-wavy way of doing this, does anyone have any suggestions to do this better? ​ https://preview.redd.it/9mnewmibbts81.png?width=375&format=png&auto=webp&s=a6ff866a31987375d276d12f69dbe2af40380bf4 https://preview.redd.it/096n7ctcbts81.png?width=375&format=png&auto=webp&s=b19d800fbb416fca288e86640d9458c8993e0759 ​ submitted by /u/nickthorpie [link] [comments]  ( 2 min )

Hey there! I'm looking for advice on datasets to use for a project. We are looking for the following traits: ​ 1) Multivariate (at least 3 or 4, and probably no more than 50 or 100 as an upper bound). 2) High frequency (Ideally at least once every 5-10 minutes) 3) We need to have some notion of an underlying 'state' of the data for certain windows. E.g. in an energy setting, period X was the 'family at home using appliances' state. Or in the healthcare setting, period X is 'the patient is in a stable state' while period Y is something like 'the patient experiences a cardiac event' ​ ​ Nice to have: 4) It'd be great if some features had some level of seasonality while others didn't. ​ ​ Do folks have any recommendations for datasets that meet some (or hopefully all) of the criteria? I did some light pursuing on UCI, but it seems like much of it is not high frequency enough, and/or doesn't have some notion of underlying states. submitted by /u/CS_Student95 [link] [comments]  ( 1 min )

Hello, I am currently working on a research paper that I aim to publish at a reputable conference shortly. In our work, we borrow a feature engineering technique from one of the papers that have not been previously applied to the domain (time series AD) before that paper. However, the authors haven't released the source code of their implementation of the model (but the feature engineering technique is publicly available). I feel like that is an important baseline and just failing to include it would get my paper rejected. I have contacted all the authors for the source code, but none of them responded. The architecture they use is a fairly complicated one and would be very difficult to implement on my own. How do I go about this situation? My advisor told me I can just include a few points on the footnote on why we don't include this as a baseline. Those being: No open-source implementation Contacted the authors, didn't receive a response The paper has not been published, only uploaded to arxiv. Any help is appreciated! submitted by /u/mythrowaway0852 [link] [comments]  ( 5 min )

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Hi all! As a sort of art experiment, I want to train an AI to write tech news articles based on my own work. I worked as a freelance writer for several years and have thousands of articles (each as a Word doc) on tech news. I want to use those articles to train the AI, then have it generate new articles to post to a blog. I have a pretty good understanding of machine learning, but have never trained a model myself. I'm hoping you all can provide some direction. Some specific questions: Can you recommend a model? For each training article, can I provide a "source" (like another news article) so the AI understands where the content in the training article came from? * For each generated article, can I provide a news article source for it to base its content on? ** Can I use the Word docs as the training set, or do I need to convert them into something else for training? *as an example: If I wrote an article on the release of a new Raspberry Pi board, my source might be the press release on the Raspberry Pi website. **as an example: If I want it to generate an article about a new drone delivery service, my input source might be a news article on Reuters or something. submitted by /u/TheSerialHobbyist [link] [comments]  ( 1 min )

Hey is there any models to generate videos for advertisment either as text-to-video images-to-video or video-variation creation, if not would video variation generative models would be a good fit for create ads ?? submitted by /u/National-Departure78 [link] [comments]

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Is there any resources which has example driven explanations, from scratch or basics? I have seen some websites just jumping into "use this module/library" Without explaining what it does or how it works, just some basic examples so that i can build on top or experiment by my own. submitted by /u/-1Mbps [link] [comments]  ( 1 min )

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Object detection is a crucial problem in computer vision, and YOLO (You Only Look Once) one-stage object detectors have set the bar for performance since the release of YOLOv1 in 2015. The YOLO series has undergone considerable network and structural improvements over the years. The most recent version, YOLOX, has attained an optimal balance of speed and accuracy on the NVIDIA Tesla V100 Tensor Core GPU. Baidu researchers have improved their earlier PP-YOLOv2 model, resulting in PP-YOLOE, a cutting-edge industrial object detector that beats YOLOv5 and YOLOX in speed and accuracy trade-off. The team’s PP-YOLOE-l variant outperforms PP-YOLOv2 by 1.9 percent AP and YOLOX-l by 1.3 percent AP on COCO datasets. The PP-YOLOv2 baseline model architecture comprises a ResNet50-vd backbone with deformable convolution, a PAN neck with an SPP layer and DropBlock, and a lightweight IoU aware head. PP-YOLOv2 assigns only one anchor box to each ground truth object, similar to YOLOv3. It is strongly reliant on hand-crafted design, which may not generalize well enough when trained on other datasets. Conversely, this technique necessitates a lot of additional hyperparameters. To overcome this problem, Baidu researchers have added an anchor-free technique to PP-YOLOv2 that tiles one anchor point on each pixel and assigns upper and lower bounds for detecting heads to assign ground facts to a matching feature map. The center of a bounding box can then be determined to choose positive samples from the closest pixels. A 4D vector is also predicted for regression, with minor model speedups and precision losses due to the changes. Continue Reading Paper: https://arxiv.org/pdf/2203.16250.pdf Github: https://github.com/PaddlePaddle/PaddleDetection submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

Hi all, I’m hoping someone could point me in The direction of an AI that I could dump all my previous song writing into that would spit out something "inspired by’ it. Mostly a bit of fun but interested in seeing what it throws back out at me. thanks in advance for any hot tips. submitted by /u/doccaballero [link] [comments]  ( 1 min )

Hi all. I have been trying to work on an object detection project. Basically trying to play around with the codes in the documentation for a custom dataset. I am using Yolov3 and I trained my model using darknet and seems like the model is learning it wrong because of which the weights are not correct either. I don't know how to check that but when doing forward propagation, the array seems to be ok, without nan values, but the confidence is mostly 0's and some 0.25's. Anyone who can guide me, on where I could have gone wrong? ​ code: https://opencv-tutorial.readthedocs.io/en/latest/yolo/yolo.html [yolov3 secion] output: outputs = [[0.03846154 0.03846154 0.27884614 0.21634616 0.5 0.25 ] [0.03846154 0.03846154 0. 0.47596154 0. 0. ] [0.03846154 0.03846154 0.89663464 0.78365386 0.5 0.25 ] ... [0.99038464 0.99038464 0.02403846 0.03125 0.5 0.25 ] [0.99038464 0.99038464 0.03846154 0.07211538 0.5 0. ] [0.99038464 0.99038464 0.07932692 0.05528846 0.5 0. ]] confidence = 0.25 0.0 0.25 0.0 0.0 0.0 0.25 0.0 0.0 0.0 0.0 0.0 0.25 0.0 0.0 0.0 0.0 0.25 0.25 0.0 0.0 0.0 0.0 0.0 0.25 0.0 0.0 0.0 0.0 0.0 0.25 0.0 0.25 0.0 0.0 0.0 0.25 0.0 0.0 0.0 0.0 0.0 0.25 0.0 0.0 0.0 0.0 0.0 0.25 0.0 0.0 0.0 0.0 0.0 0.25 0.0 0.0 0.0 0.0 0.0 0.25 0.0 0.0 0.0 0.0 0.0 0.25 0.0 0.0 0.0 0.0 0.0 0.25 0.0 0.0 0.0 0.0 0.0 0.25 0.0 0.0 0.0 0.0 0.0 0.25 ... submitted by /u/ersa17 [link] [comments]  ( 1 min )

Graph neural networks (GNNs) have received massive attention in the field of machine learning on graphs. Inspired by the success of neural networks, a line of research has been conducted to train GNNs to deal with various tasks, such as node classification, graph classification, and link prediction. In this work, our task of interest is graph classification. Several GNN models have been proposed and shown great accuracy in this task. However, the question is whether usual training methods fully realize the capacity of the GNN models. In this work, we propose a two-stage training framework based on triplet loss. In the first stage, GNN is trained to map each graph to a Euclidean-space vector so that graphs of the same class are close while those of different classes are mapped far apart. Once graphs are well-separated based on labels, a classifier is trained to distinguish between different classes. This method is generic in the sense that it is compatible with any GNN model. By adapting five GNN models to our method, we demonstrate the consistent improvement in accuracy and utilization of each GNN's allocated capacity over the original training method of each model up to 5.4\% points in 12 datasets.  ( 2 min )

Various non-trivial spaces are becoming popular for embedding structured data such as graphs, texts, or images. Following spherical and hyperbolic spaces, more general product spaces have been proposed. However, searching for the best configuration of product space is a resource-intensive procedure, which reduces the practical applicability of the idea. We generalize the concept of product space and introduce an overlapping space that does not have the configuration search problem. The main idea is to allow subsets of coordinates to be shared between spaces of different types (Euclidean, hyperbolic, spherical). As a result, parameter optimization automatically learns the optimal configuration. Additionally, overlapping spaces allow for more compact representations since their geometry is more complex. Our experiments confirm that overlapping spaces outperform the competitors in graph embedding tasks. Here, we consider both distortion setup, where the aim is to preserve distances, and ranking setup, where the relative order should be preserved. The proposed method effectively solves the problem and outperforms the competitors in both settings. We also perform an empirical analysis in a realistic information retrieval task, where we compare all spaces by incorporating them into DSSM. In this case, the proposed overlapping space consistently achieves nearly optimal results without any configuration tuning. This allows for reducing training time, which can be significant in large-scale applications.  ( 2 min )

Neural Architecture Search (NAS) has shifted network design from using human intuition to leveraging search algorithms guided by evaluation metrics. We study channel size optimization in convolutional neural networks (CNN) and identify the role it plays in model accuracy and complexity. Current channel size selection methods are generally limited by discrete sample spaces while suffering from manual iteration and simple heuristics. To solve this, we introduce an efficient dynamic scaling algorithm -- CONet -- that automatically optimizes channel sizes across network layers for a given CNN. Two metrics -- "\textit{Rank}" and "\textit{Rank Average Slope}" -- are introduced to identify the information accumulated in training. The algorithm dynamically scales channel sizes up or down over a fixed searching phase. We conduct experiments on CIFAR10/100 and ImageNet datasets and show that CONet can find efficient and accurate architectures searched in ResNet, DARTS, and DARTS+ spaces that outperform their baseline models. This document supersedes previously published paper in ICCV2021-NeurArch workshop. An additional section is included on manual scaling of channel size in CNNs to numerically validate of the metrics used in searching optimum channel configurations in CNNs.  ( 2 min )

Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem while also providing uncertainty quantification. The most popular inference algorithms for SBL exhibit prohibitively large computational costs for high-dimensional problems due to the need to maintain a large covariance matrix. To resolve this issue, we introduce a new method for accelerating SBL inference -- named covariance-free expectation maximization (CoFEM) -- that avoids explicit computation of the covariance matrix. CoFEM solves multiple linear systems to obtain unbiased estimates of the posterior statistics needed by SBL. This is accomplished by exploiting innovations from numerical linear algebra such as preconditioned conjugate gradient and a little-known diagonal estimation rule. For a large class of compressed sensing matrices, we provide theoretical justifications for why our method scales well in high-dimensional settings. Through simulations, we show that CoFEM can be up to thousands of times faster than existing baselines without sacrificing coding accuracy. Through applications to calcium imaging deconvolution and multi-contrast MRI reconstruction, we show that CoFEM enables SBL to tractably tackle high-dimensional sparse coding problems of practical interest.  ( 2 min )

Simulation-based inference enables learning the parameters of a model even when its likelihood cannot be computed in practice. One class of methods uses data simulated with different parameters to infer models of the likelihood-to-evidence ratio, or equivalently the posterior function. Here we frame the inference task as an estimation of an energy function parametrized with an artificial neural network. We present an intuitive approach where the optimal model of the likelihood-to-evidence ratio is found by maximizing the likelihood of simulated data. Within this framework, the connection between the task of simulation-based inference and mutual information maximization is clear, and we show how several known methods of posterior estimation relate to alternative lower bounds to mutual information. These distinct objective functions aim at the same optimal energy form and therefore can be directly benchmarked. We compare their accuracy in the inference of model parameters, focusing on four dynamical systems that encompass common challenges in time series analysis: dynamics driven by multiplicative noise, nonlinear interactions, chaotic behavior, and high-dimensional parameter space.  ( 2 min )

We consider the problem of learning high dimensional polynomial transformations of Gaussians. Given samples of the form $p(x)$, where $x\sim N(0, \mathrm{Id}_r)$ is hidden and $p: \mathbb{R}^r \to \mathbb{R}^d$ is a function where every output coordinate is a low-degree polynomial, the goal is to learn the distribution over $p(x)$. This problem is natural in its own right, but is also an important special case of learning deep generative models, namely pushforwards of Gaussians under two-layer neural networks with polynomial activations. Understanding the learnability of such generative models is crucial to understanding why they perform so well in practice. Our first main result is a polynomial-time algorithm for learning quadratic transformations of Gaussians in a smoothed setting. Our second main result is a polynomial-time algorithm for learning constant-degree polynomial transformations of Gaussian in a smoothed setting, when the rank of the associated tensors is small. In fact our results extend to any rotation-invariant input distribution, not just Gaussian. These are the first end-to-end guarantees for learning a pushforward under a neural network with more than one layer. Along the way, we also give the first polynomial-time algorithms with provable guarantees for tensor ring decomposition, a popular generalization of tensor decomposition that is used in practice to implicitly store large tensors.  ( 2 min )

Current test and evaluation (T&E) methods for assessing machine learning (ML) system performance often rely on incomplete metrics. Testing is additionally often siloed from the other phases of the ML system lifecycle. Research investigating cross-domain approaches to ML T&E is needed to drive the state of the art forward and to build an Artificial Intelligence (AI) engineering discipline. This paper advocates for a robust, integrated approach to testing by outlining six key questions for guiding a holistic T&E strategy.  ( 2 min )

This work focuses on the use of deep learning for vulnerability analysis of cyber-physical systems (CPS). Specifically, we consider a control architecture widely used in CPS (e.g., robotics), where the low-level control is based on e.g., the extended Kalman filter (EKF) and an anomaly detector. To facilitate analyzing the impact potential sensing attacks could have, our objective is to develop learning-enabled attack generators capable of designing stealthy attacks that maximally degrade system operation. We show how such problem can be cast within a learning-based grey-box framework where parts of the runtime information are known to the attacker, and introduce two models based on feed-forward neural networks (FNN); both models are trained offline, using a cost function that combines the attack effects on the estimation error and the residual signal used for anomaly detection, so that the trained models are capable of recursively generating such effective sensor attacks in real-time. The effectiveness of the proposed methods is illustrated on several case studies.  ( 2 min )

The next generation of physical science involves robot scientists - autonomous physical science systems capable of experimental design, execution, and analysis in a closed loop. Such systems have shown real-world success for scientific exploration and discovery, including the first discovery of a best-in-class material. To build and use these systems, the next generation workforce requires expertise in diverse areas including ML, control systems, measurement science, materials synthesis, decision theory, among others. However, education is lagging. Educators need a low-cost, easy-to-use platform to teach the required skills. Industry can also use such a platform for developing and evaluating autonomous physical science methodologies. We present the next generation in science education, a kit for building a low-cost autonomous scientist. The kit was used during two courses at the University of Maryland to teach undergraduate and graduate students autonomous physical science. We discuss its use in the course and its greater capability to teach the dual tasks of autonomous model exploration, optimization, and determination, with an example of autonomous experimental "discovery" of the Henderson-Hasselbalch equation.  ( 2 min )

Learning good quality neural graph embeddings has long been achieved by minimzing the pointwise mutual information (PMI) for co-occuring nodes in simulated random walks. This design choice has been mostly popularized by the direct application of the highly-successful word embedding algorithm word2vec to predicting the formation of new links in social, co-citation, and biological networks. However, such a skeumorphic design of graph embedding methods entails a truncation of information coming from pairs of nodes with low PMI. To circumvent this issue, we propose an improved approach to learning low-rank factorization embeddings that incorporate information from such unlikely pairs of nodes and show that it can improve the link prediction performance of baseline methods from 1.2% to 24.2%. Based on our results and observations we outline further steps that could improve the design of next graph embedding algorithms that are based on matrix factorizaion.  ( 2 min )

Contrastive learning enables learning useful audio and speech representations without ground-truth labels by maximizing the similarity between latent representations of similar signal segments. In this framework various data augmentation techniques are usually exploited to help enforce desired invariances within the learned representations, improving performance on various audio tasks thanks to more robust embeddings. Now, selecting the most relevant augmentations has proven crucial for better downstream performances. Thus, this work introduces a conditional independance-based method which allows for automatically selecting a suitable distribution on the choice of augmentations and their parametrization from a set of predefined ones, for contrastive self-supervised pre-training. This is performed with respect to a downstream task of interest, hence saving a costly hyper-parameter search. Experiments performed on two different downstream tasks validate the proposed approach showing better results than experimenting without augmentation or with baseline augmentations. We furthermore conduct a qualitative analysis of the automatically selected augmentations and their variation according to the considered final downstream dataset.  ( 2 min )

In representation learning, there has been recent interest in developing algorithms to disentangle the ground-truth generative factors behind a dataset, and metrics to quantify how fully this occurs. However, these algorithms and metrics often assume that both representations and ground-truth factors are flat, continuous, and factorized, whereas many real-world generative processes involve rich hierarchical structure, mixtures of discrete and continuous variables with dependence between them, and even varying intrinsic dimensionality. In this work, we develop benchmarks, algorithms, and metrics for learning such hierarchical representations.  ( 2 min )

This paper presents a constrained adaptive dynamic programming (CADP) algorithm to solve general nonlinear nonaffine optimal control problems with known dynamics. Unlike previous ADP algorithms, it can directly deal with problems with state constraints. Firstly, a constrained generalized policy iteration (CGPI) framework is developed to handle state constraints by transforming the traditional policy improvement process into a constrained policy optimization problem. Next, we propose an actor-critic variant of CGPI, called CADP, in which both policy and value functions are approximated by multi-layer neural networks to directly map the system states to control inputs and value function, respectively. CADP linearizes the constrained optimization problem locally into a quadratically constrained linear programming problem, and then obtains the optimal update of the policy network by solving its dual problem. A trust region constraint is added to prevent excessive policy update, thus ensuring linearization accuracy. We determine the feasibility of the policy optimization problem by calculating the minimum trust region boundary and update the policy using two recovery rules when infeasible. The vehicle control problem in the path-tracking task is used to demonstrate the effectiveness of this proposed method.  ( 2 min )

The success and popularity of deep learning is on the rise, partially due to powerful deep learning frameworks such as TensorFlow and PyTorch that make it easier to develop deep learning models. However, these libraries also come with steep learning curves, since programming in these frameworks is quite different from traditional imperative programming with explicit loops and conditionals. In this work, we present a tool called TF-Coder for programming by example in TensorFlow. TF-Coder uses a bottom-up weighted enumerative search, with value-based pruning of equivalent expressions and flexible type- and value-based filtering to ensure that expressions adhere to various requirements imposed by the TensorFlow library. We train models to predict TensorFlow operations from features of the input and output tensors and natural language descriptions of tasks, to prioritize relevant operations during search. TF-Coder solves 63 of 70 real-world tasks within 5 minutes, sometimes finding simpler solutions in less time compared to experienced human programmers.  ( 2 min )

Existing singing voice synthesis models (SVS) are usually trained on singing data and depend on either error-prone time-alignment and duration features or explicit music score information. In this paper, we propose Karaoker, a multispeaker Tacotron-based model conditioned on voice characteristic features that is trained exclusively on spoken data without requiring time-alignments. Karaoker synthesizes singing voice following a multi-dimensional template extracted from a source waveform of an unseen speaker/singer. The model is jointly conditioned with a single deep convolutional encoder on continuous data including pitch, intensity, harmonicity, formants, cepstral peak prominence and octaves. We extend the text-to-speech training objective with feature reconstruction, classification and speaker identification tasks that guide the model to an accurate result. Except for multi-tasking, we also employ a Wasserstein GAN training scheme as well as new losses on the acoustic model's output to further refine the quality of the model.  ( 2 min )

We study the stochastic bilinear minimax optimization problem, presenting an analysis of the same-sample Stochastic ExtraGradient (SEG) method with constant step size, and presenting variations of the method that yield favorable convergence. In sharp contrasts with the basic SEG method whose last iterate only contracts to a fixed neighborhood of the Nash equilibrium, SEG augmented with iteration averaging provably converges to the Nash equilibrium under the same standard settings, and such a rate is further improved by incorporating a scheduled restarting procedure. In the interpolation setting where noise vanishes at the Nash equilibrium, we achieve an optimal convergence rate up to tight constants. We present numerical experiments that validate our theoretical findings and demonstrate the effectiveness of the SEG method when equipped with iteration averaging and restarting.  ( 2 min )

Significant effort has been made to solve computationally expensive optimization problems in the past two decades, and various optimization methods incorporating surrogates into optimization have been proposed. Most research focuses on either exploiting the surrogate by defining a utility optimization problem or customizing an existing optimization method to use one or multiple approximation models. However, only a little attention has been paid to generic concepts applicable to different types of algorithms and optimization problems simultaneously. Thus this paper proposes a generalized probabilistic surrogate-assisted framework (GPSAF), applicable to a broad category of unconstrained and constrained, single- and multi-objective optimization algorithms. The idea is based on a surrogate assisting an existing optimization method. The assistance is based on two distinct phases, one facilitating exploration and another exploiting the surrogates. The exploration and exploitation of surrogates are automatically balanced by performing a probabilistic knockout tournament among different clusters of solutions. A study of multiple well-known population-based optimization algorithms is conducted with and without the proposed surrogate assistance on single- and multi-objective optimization problems with a maximum solution evaluation budget of 300 or less. The results indicate the effectiveness of applying GPSAF to an optimization algorithm and the competitiveness with other surrogate-assisted algorithms.  ( 2 min )

Behavioural testing -- verifying system capabilities by validating human-designed input-output pairs -- is an alternative evaluation method of natural language processing systems proposed to address the shortcomings of the standard approach: computing metrics on held-out data. While behavioural tests capture human prior knowledge and insights, there has been little exploration on how to leverage them for model training and development. With this in mind, we explore behaviour-aware learning by examining several fine-tuning schemes using HateCheck, a suite of functional tests for hate speech detection systems. To address potential pitfalls of training on data originally intended for evaluation, we train and evaluate models on different configurations of HateCheck by holding out categories of test cases, which enables us to estimate performance on potentially overlooked system properties. The fine-tuning procedure led to improvements in the classification accuracy of held-out functionalities and identity groups, suggesting that models can potentially generalise to overlooked functionalities. However, performance on held-out functionality classes and i.i.d. hate speech detection data decreased, which indicates that generalisation occurs mostly across functionalities from the same class and that the procedure led to overfitting to the HateCheck data distribution.  ( 2 min )

This paper is concerned with the training of neural networks (NNs) under semidefinite constraints, which allows for NN training with robustness and stability guarantees. In particular, we set up an efficient and scalable training scheme for NN training problems of this kind based on interior point methods, while we also exploit the structure of the underlying matrix constraint. We apply our training scheme to several relevant examples that have been studied in the literature and newly present the application of the method to the training of Wasserstein generative adversarial networks (WGANs). In numerical examples, we show the superiority of our method and its applicability to WGAN training.

The real-time prediction of business processes using historical event data is an important capability of modern business process monitoring systems. Existing process prediction methods are able to also exploit the data perspective of recorded events, in addition to the control-flow perspective. However, while well-structured numerical or categorical attributes are considered in many prediction techniques, almost no technique is able to utilize text documents written in natural language, which can hold information critical to the prediction task. In this paper, we illustrate the design, implementation, and evaluation of a novel text-aware process prediction model based on Long Short-Term Memory (LSTM) neural networks and natural language models. The proposed model can take categorical, numerical and textual attributes in event data into account to predict the activity and timestamp of the next event, the outcome, and the cycle time of a running process instance. Experiments show that the text-aware model is able to outperform state-of-the-art process prediction methods on simulated and real-world event logs containing textual data.

This article proposes a sparse computation-based method for optimizing neural networks for reinforcement learning (RL) tasks. This method combines two ideas: neural network pruning and taking into account input data correlations; it makes it possible to update neuron states only when changes in them exceed a certain threshold. It significantly reduces the number of multiplications when running neural networks. We tested different RL tasks and achieved 20-150x reduction in the number of multiplications. There were no substantial performance losses; sometimes the performance even improved.

The adversarial risk of a machine learning model has been widely studied. Most previous works assume that the data lies in the whole ambient space. We propose to take a new angle and take the manifold assumption into consideration. Assuming data lies in a manifold, we investigate two new types of adversarial risk, the normal adversarial risk due to perturbation along normal direction, and the in-manifold adversarial risk due to perturbation within the manifold. We prove that the classic adversarial risk can be bounded from both sides using the normal and in-manifold adversarial risks. We also show with a surprisingly pessimistic case that the standard adversarial risk can be nonzero even when both normal and in-manifold risks are zero. We finalize the paper with empirical studies supporting our theoretical results. Our results suggest the possibility of improving the robustness of a classifier by only focusing on the normal adversarial risk.

Most semi-supervised learning methods over-sample labeled data when constructing training mini-batches. This paper studies whether this common practice improves learning and how. We compare it to an alternative setting where each mini-batch is uniformly sampled from all the training data, labeled or not, which greatly reduces direct supervision from true labels in typical low-label regimes. However, this simpler setting can also be seen as more general and even necessary in multi-task problems where over-sampling labeled data would become intractable. Our experiments on semi-supervised CIFAR-10 image classification using FixMatch show a performance drop when using the uniform sampling approach which diminishes when the amount of labeled data or the training time increases. Further, we analyse the training dynamics to understand how over-sampling of labeled data compares to uniform sampling. Our main finding is that over-sampling is especially beneficial early in training but gets less important in the later stages when more pseudo-labels become correct. Nevertheless, we also find that keeping some true labels remains important to avoid the accumulation of confirmation errors from incorrect pseudo-labels.

Deep neuroevolution and deep Reinforcement Learning have received a lot of attention in the last years. Some works have compared them, highlighting theirs pros and cons, but an emerging trend consists in combining them so as to benefit from the best of both worlds. In this paper, we provide a survey of this emerging trend by organizing the literature into related groups of works and casting all the existing combinations in each group into a generic framework. We systematically cover all easily available papers irrespective of their publication status, focusing on the combination mechanisms rather than on the experimental results. In total, we cover 45 algorithms more recent than 2017. We hope this effort will favor the growth of the domain by facilitating the understanding of the relationships between the methods, leading to deeper analyses, outlining missing useful comparisons and suggesting new combinations of mechanisms.

The recent development of Generative adversarial networks (GANs) has driven many computer vision applications. Despite the great synthesis quality, training GANs often confronts several issues, including non-convergence, mode collapse, and gradient vanishing. There exist several workarounds, for example, regularizing Lipschitz continuity and adopting Wasserstein distance. Although these methods can partially solve the problems, we argue that the problems are result from modeling the discriminator with deep neural networks. In this paper, we base on newly derived deep neural network theories called Neural Tangent Kernel (NTK) and propose a new generative algorithm called generative adversarial NTK (GA-NTK). The GA-NTK models the discriminator as a Gaussian Process (GP). With the help of the NTK theories, the training dynamics of GA-NTK can be described with a closed-form formula. To synthesize data with the closed-form formula, the objectives can be simplified into a single-level adversarial optimization problem. We conduct extensive experiments on real-world datasets, and the results show that GA-NTK can generate images comparable to those by GANs but is much easier to train under various conditions. We also study the current limitations of GA-NTK and propose some workarounds to make GA-NTK more practical.  ( 2 min )

Submodular maximization has been the backbone of many important machine-learning problems, and has applications to viral marketing, diversification, sensor placement, and more. However, the study of maximizing submodular functions has mainly been restricted in the context of selecting a set of items. On the other hand, many real-world applications require a solution that is a ranking over a set of items. The problem of ranking in the context of submodular function maximization has been considered before, but to a much lesser extent than item-selection formulations. In this paper, we explore a novel formulation for ranking items with submodular valuations and budget constraints. We refer to this problem as max-submodular ranking (MSR). In more detail, given a set of items and a set of non-decreasing submodular functions, where each function is associated with a budget, we aim to find a ranking of the set of items that maximizes the sum of values achieved by all functions under the budget constraints. For the MSR problem with cardinality- and knapsack-type budget constraints we propose practical algorithms with approximation guarantees. In addition, we perform an empirical evaluation, which demonstrates the superior performance of the proposed algorithms against strong baselines.  ( 2 min )

Deep learning has proven effective across a range of data sets. In light of this, a natural inquiry is: "for what data generating processes can deep learning succeed?" In this work, we study the sample complexity of learning multilayer data generating processes of a sort for which deep neural networks seem to be suited. We develop general and elegant information-theoretic tools that accommodate analysis of any data generating process -- shallow or deep, parametric or nonparametric, noiseless or noisy. We then use these tools to characterize the dependence of sample complexity on the depth of multilayer processes. Our results indicate roughly linear dependence on depth. This is in contrast to previous results that suggest exponential or high-order polynomial dependence.

We investigate a new threat to neural sequence-to-sequence (seq2seq) models: training-time attacks that cause models to "spin" their outputs so as to support an adversary-chosen sentiment or point of view -- but only when the input contains adversary-chosen trigger words. For example, a spinned summarization model outputs positive summaries of any text that mentions the name of some individual or organization. Model spinning introduces a "meta-backdoor" into a model. Whereas conventional backdoors cause models to produce incorrect outputs on inputs with the trigger, outputs of spinned models preserve context and maintain standard accuracy metrics, yet also satisfy a meta-task chosen by the adversary. Model spinning enables propaganda-as-a-service, where propaganda is defined as biased speech. An adversary can create customized language models that produce desired spins for chosen triggers, then deploy these models to generate disinformation (a platform attack), or else inject them into ML training pipelines (a supply-chain attack), transferring malicious functionality to downstream models trained by victims. To demonstrate the feasibility of model spinning, we develop a new backdooring technique. It stacks an adversarial meta-task onto a seq2seq model, backpropagates the desired meta-task output to points in the word-embedding space we call "pseudo-words," and uses pseudo-words to shift the entire output distribution of the seq2seq model. We evaluate this attack on language generation, summarization, and translation models with different triggers and meta-tasks such as sentiment, toxicity, and entailment. Spinned models largely maintain their accuracy metrics (ROUGE and BLEU) while shifting their outputs to satisfy the adversary's meta-task. We also show that, in the case of a supply-chain attack, the spin functionality transfers to downstream models.

There is a growing interest in applying machine learning techniques for healthcare. Recently, federated machine learning (FL) is gaining popularity since it allows researchers to train powerful models without compromising data privacy and security. However, the performance of existing FL approaches often deteriorates when encountering non-iid situations where there exist distribution gaps among clients, and few previous efforts focus on personalization in healthcare. In this article, we propose AdaFed to tackle domain shifts and obtain personalized models for local clients. AdaFed learns the similarity between clients via the statistics of the batch normalization layers while preserving the specificity of each client with different local batch normalization. Comprehensive experiments on five healthcare benchmarks demonstrate that AdaFed achieves better accuracy compared to state-of-the-art methods (e.g., \textbf{10}\%+ accuracy improvement for PAMAP2) with faster convergence speed.

Interactive object understanding, or what we can do to objects and how is a long-standing goal of computer vision. In this paper, we tackle this problem through observation of human hands in in-the-wild egocentric videos. We demonstrate that observation of what human hands interact with and how can provide both the relevant data and the necessary supervision. Attending to hands, readily localizes and stabilizes active objects for learning and reveals places where interactions with objects occur. Analyzing the hands shows what we can do to objects and how. We apply these basic principles on the EPIC-KITCHENS dataset, and successfully learn state-sensitive features, and object affordances (regions of interaction and afforded grasps), purely by observing hands in egocentric videos.

Disease-modifying management aims to prevent deterioration and progression of the disease, not just relieve symptoms. Unfortunately, the development of necessary therapies is often hampered by the failure to recognize the presymptomatic disease and limited understanding of disease development. We present a generic solution for this problem by a methodology that allows the prediction of progression risk and morphology in individuals using a latent extrapolation optimization approach. To this end, we combined a regularized generative adversarial network (GAN) and a latent nearest neighbor algorithm for joint optimization to generate plausible images of future time points. We evaluated our method on osteoarthritis (OA) data from a multi-center longitudinal study (the Osteoarthritis Initiative, OAI). With presymptomatic baseline data, our model is generative and significantly outperforms the end-to-end learning model in discriminating the progressive cohort. Two experiments were performed with seven experienced radiologists. When no synthetic follow-up radiographs were provided, our model performed better than all seven radiologists. In cases where the synthetic follow-ups generated by our model were available, the specificity and sensitivity of all readers in discriminating progressors increased from $72.3\%$ to $88.6\%$ and from $42.1\%$ to $51.6\%$, respectively. Our results open up a new possibility of using model-based morphology and risk prediction to make predictions about future disease occurrence, as demonstrated in the example of OA.

Speech enhancement (SE) aims to suppress the additive noise from a noisy speech signal to improve the speech's perceptual quality and intelligibility. However, the over-suppression phenomenon in the enhanced speech might degrade the performance of downstream automatic speech recognition (ASR) task due to the missing latent information. To alleviate such problem, we propose an interactive feature fusion network (IFF-Net) for noise-robust speech recognition to learn complementary information from the enhanced feature and original noisy feature. Experimental results show that the proposed method achieves absolute word error rate (WER) reduction of 4.1% over the best baseline on RATS Channel-A corpus. Our further analysis indicates that the proposed IFF-Net can complement some missing information in the over-suppressed enhanced feature.

We study active sampling algorithms for linear regression, which aim to query only a few entries of a target vector $b\in\mathbb R^n$ and output a near minimizer to $\min_{x\in\mathbb R^d} \|Ax-b\|$, for a design matrix $A\in\mathbb R^{n \times d}$ and loss $\|\cdot\|$. For $p$ norm regression for any $0<p<\infty$, we give an algorithm based on Lewis weight sampling outputting a $(1+\epsilon)$-approximate solution using just $\tilde O(d/\epsilon^2)$ queries to $b$ for $p\in(0,1)$, $\tilde{O}(d/\epsilon)$ queries for $1<p<2$, and $\tilde{O}(d^{p/2}/\epsilon^p)$ queries for $2<p<\infty$. For $0<p<2$, our bounds are optimal up to log factors, settling the query complexity for this range. For $2<p<\infty$, our dependence on $d$ is optimal, while our dependence on $\epsilon$ is off by at most $\epsilon$, up to log factors. Our result resolves an open question of [CD21], who gave near optimal bounds for the $1$ norm, but required $d^2/\epsilon^2$ samples for $\ell_p$ regression with $1<p<2$, and gave no bounds for $2<p<\infty$ or $0<p<1$. We also give the first total sensitivity bound of $O(d^{\max\{1,p/2\}}\log^2n)$ for loss functions of degree $p$ polynomial growth, improving a result of [TMF20]. By combining this with our techniques for $\ell_p$ regression, we obtain an active regression algorithm making $\tilde O(d^{1+\max\{1,p/2\}}/\mathrm{poly}(\epsilon))$ queries for such loss functions, including the Tukey and Huber losses, answering another question of [CD21]. For the Huber loss, we further improve our bound to $\tilde O(d^{4-2\sqrt2}/\mathrm{poly}(\epsilon))$ samples. Our sensitivity bounds also have many applications, including Orlicz norm subspace embeddings, robust subspace approximation, and dimension reduction for smoothed $p$-norms. Finally, our active sampling results give the first sublinear time algorithms for Kronecker product regression under every $p$ norm.

Deep models are highly susceptible to adversarial attacks. Such attacks are carefully crafted imperceptible noises that can fool the network and can cause severe consequences when deployed. To encounter them, the model requires training data for adversarial training or explicit regularization-based techniques. However, privacy has become an important concern, restricting access to only trained models but not the training data (e.g. biometric data). Also, data curation is expensive and companies may have proprietary rights over it. To handle such situations, we propose a completely novel problem of 'test-time adversarial defense in absence of training data and even their statistics'. We solve it in two stages: a) detection and b) correction of adversarial samples. Our adversarial sample detection framework is initially trained on arbitrary data and is subsequently adapted to the unlabelled test data through unsupervised domain adaptation. We further correct the predictions on detected adversarial samples by transforming them in Fourier domain and obtaining their low frequency component at our proposed suitable radius for model prediction. We demonstrate the efficacy of our proposed technique via extensive experiments against several adversarial attacks and for different model architectures and datasets. For a non-robust Resnet-18 model pre-trained on CIFAR-10, our detection method correctly identifies 91.42% adversaries. Also, we significantly improve the adversarial accuracy from 0% to 37.37% with a minimal drop of 0.02% in clean accuracy on state-of-the-art 'Auto Attack' without having to retrain the model.

Disentangled representation learning offers useful properties such as dimension reduction and interpretability, which are essential to modern deep learning approaches. Although deep learning techniques have been widely applied to spatio-temporal data mining, there has been little attention to further disentangle the latent features and understanding their contribution to the model performance, particularly their mutual information and correlation across features. In this study, we adopt two state-of-the-art disentangled representation learning methods and apply them to three large-scale public spatio-temporal datasets. To evaluate their performance, we propose an internal evaluation metric focusing on the degree of correlations among latent variables of the learned representations and the prediction performance of the downstream tasks. Empirical results show that our modified method can learn disentangled representations that achieve the same level of performance as existing state-of-the-art ST deep learning methods in a spatio-temporal sequence forecasting problem. Additionally, we find that our methods can be used to discover real-world spatial-temporal semantics to describe the variables in the learned representation.

The noise transition matrix plays a central role in the problem of learning from noisy labels. Among many other reasons, a significant number of existing solutions rely on access to it. Estimating the transition matrix without using ground truth labels is a critical and challenging task. When label noise transition depends on each instance, the problem of identifying the instance-dependent noise transition matrix becomes substantially more challenging. Despite recent works proposing solutions for learning from instance-dependent noisy labels, we lack a unified understanding of when such a problem remains identifiable, and therefore learnable. This paper seeks to provide answers to a sequence of related questions: What are the primary factors that contribute to the identifiability of a noise transition matrix? Can we explain the observed empirical successes? When a problem is not identifiable, what can we do to make it so? We will relate our theoretical findings to the literature and hope to provide guidelines for developing effective solutions for battling instance-dependent label noise.

Ride-hailing service is becoming a leading part in urban transportation. To improve the efficiency of ride-hailing service, accurate prediction of transportation demand is a fundamental challenge. In this paper, we tackle this problem from both aspects of network structure and data-set formulation. For network design, we propose a spatial-temporal attention multi-graph convolution network (STA-MGCN). A spatial-temporal layer in STA-MGCN is developed to capture the temporal correlations by temporal attention mechanism and temporal gate convolution, and the spatial correlations by multigraph convolution. A feature cluster layer is introduced to learn latent regional functions and to reduce the computation burden. For the data-set formulation, we develop a novel approach which considers the transportation feature of periodicity with offset. Instead of only using history data during the same time period, the history order demand in forward and backward neighboring time periods from yesterday and last week are also included. Extensive experiments on the three real-world datasets of New-York, Chicago and Chengdu show that the proposed algorithm achieves the state-of-the-art performance for ride-hailing demand prediction.

In the last few years, the memory requirements to train state-of-the-art neural networks have far exceeded the DRAM capacities of modern hardware accelerators. This has necessitated the development of efficient algorithms to train these neural networks in parallel on large-scale GPU-based clusters. Since computation is relatively inexpensive on modern GPUs, designing and implementing extremely efficient communication in these parallel training algorithms is critical for extracting the maximum performance. This paper presents AxoNN, a parallel deep learning framework that exploits asynchrony and message-driven execution to schedule neural network operations on each GPU, thereby reducing GPU idle time and maximizing hardware efficiency. By using the CPU memory as a scratch space for offloading data periodically during training, AxoNN is able to reduce GPU memory consumption by four times. This allows us to increase the number of parameters per GPU by four times, thus reducing the amount of communication and increasing performance by over 13%. When tested against large transformer models with 12-100 billion parameters on 48-384 NVIDIA Tesla V100 GPUs, AxoNN achieves a per-GPU throughput of 49.4-54.78% of theoretical peak and reduces the training time by 22-37 days (15-25% speedup) as compared to the state-of-the-art.

Analyzing observational data from multiple sources can be useful for increasing statistical power to detect a treatment effect; however, practical constraints such as privacy considerations may restrict individual-level information sharing across data sets. This paper develops federated methods that only utilize summary-level information from heterogeneous data sets. Our federated methods provide doubly-robust point estimates of treatment effects as well as variance estimates. We derive the asymptotic distributions of our federated estimators, which are shown to be asymptotically equivalent to the corresponding estimators from the combined, individual-level data. We show that to achieve these properties, federated methods should be adjusted based on conditions such as whether models are correctly specified and stable across heterogeneous data sets.

Recent work building open-domain chatbots has demonstrated that increasing model size improves performance. On the other hand, latency and connectivity considerations dictate the move of digital assistants on the device. Giving a digital assistant like Siri, Alexa, or Google Assistant the ability to discuss just about anything leads to the need for reducing the chatbot model size such that it fits on the user's device. We demonstrate that low parameter models can simultaneously retain their general knowledge conversational abilities while improving in a specific domain. Additionally, we propose a generic framework that accounts for variety in question types, tracks reference throughout multi-turn conversations, and removes inconsistent and potentially toxic responses. Our framework seamlessly transitions between chatting and performing transactional tasks, which will ultimately make interactions with digital assistants more human-like. We evaluate our framework on 1 internal and 4 public benchmark datasets using both automatic (Perplexity) and human (SSA - Sensibleness and Specificity Average) evaluation metrics and establish comparable performance while reducing model parameters by 90%.

Forensic analysis of manipulated pixels requires the identification of various hidden and subtle features from images. Conventional image recognition models generally fail at this task because they are biased and more attentive toward the dominant local and spatial features. In this paper, we propose a novel Gated Context Attention Network (GCA-Net) that utilizes non-local attention in conjunction with a gating mechanism in order to capture the finer image discrepancies and better identify forged regions. The proposed framework uses high dimensional embeddings to filter and aggregate the relevant context from coarse feature maps at various stages of the decoding process. This improves the network's understanding of global differences and reduces false-positive localizations. Our evaluation on standard image forensic benchmarks shows that GCA-Net can both compete against and improve over state-of-the-art networks by an average of 4.7% AUC. Additional ablation studies also demonstrate the method's robustness against attributions and resilience to false-positive predictions.

Machine learning systems generally assume that the training and testing distributions are the same. To this end, a key requirement is to develop models that can generalize to unseen distributions. Domain generalization (DG), i.e., out-of-distribution generalization, has attracted increasing interests in recent years. Domain generalization deals with a challenging setting where one or several different but related domain(s) are given, and the goal is to learn a model that can generalize to an unseen test domain. Great progress has been made in the area of domain generalization for years. This paper presents the first review of recent advances in this area. First, we provide a formal definition of domain generalization and discuss several related fields. We then thoroughly review the theories related to domain generalization and carefully analyze the theory behind generalization. We categorize recent algorithms into three classes: data manipulation, representation learning, and learning strategy, and present several popular algorithms in detail for each category. Third, we introduce the commonly used datasets, applications, and our open-sourced codebase for fair evaluation. Finally, we summarize existing literature and present some potential research topics for the future.

Describing images using natural language is widely known as image captioning, which has made consistent progress due to the development of computer vision and natural language generation techniques. Though conventional captioning models achieve high accuracy based on popular metrics, i.e., BLEU, CIDEr, and SPICE, the ability of captions to distinguish the target image from other similar images is under-explored. To generate distinctive captions, a few pioneers employ contrastive learning or re-weighted the ground-truth captions, which focuses on one single input image. However, the relationships between objects in a similar image group (e.g., items or properties within the same album or fine-grained events) are neglected. In this paper, we improve the distinctiveness of image captions using a Group-based Distinctive Captioning Model (GdisCap), which compares each image with other images in one similar group and highlights the uniqueness of each image. In particular, we propose a group-based memory attention (GMA) module, which stores object features that are unique among the image group (i.e., with low similarity to objects in other images). These unique object features are highlighted when generating captions, resulting in more distinctive captions. Furthermore, the distinctive words in the ground-truth captions are selected to supervise the language decoder and GMA. Finally, we propose a new evaluation metric, distinctive word rate (DisWordRate) to measure the distinctiveness of captions. Quantitative results indicate that the proposed method significantly improves the distinctiveness of several baseline models, and achieves the state-of-the-art performance on both accuracy and distinctiveness. Results of a user study agree with the quantitative evaluation and demonstrate the rationality of the new metric DisWordRate.

Through solving pretext tasks, self-supervised learning leverages unlabeled data to extract useful latent representations replacing traditional input features in the downstream task. In audio/speech signal processing, a wide range of features where engineered through decades of research efforts. As it turns out, learning to predict such features (a.k.a pseudo-labels) has proven to be a particularly relevant pretext task, leading to useful self-supervised representations which prove to be effective for downstream tasks. However, methods and common practices for combining such pretext tasks for better performance on the downstream task have not been explored and understood properly. In fact, the process relies almost exclusively on a computationally heavy experimental procedure, which becomes intractable with the increase of the number of pretext tasks. This paper introduces a method to select a group of pretext tasks among a set of candidates. The method we propose estimates calibrated weights for the partial losses corresponding to the considered pretext tasks during the self-supervised training process. The experiments conducted on automatic speech recognition, speaker and emotion recognition validate our approach, as the groups selected and weighted with our method perform better than classic baselines, thus facilitating the selection and combination of relevant pseudo-labels for self-supervised representation learning.

Learning a generalizable deep model from a few examples in a short time remains a major challenge of machine learning, which has impeded its wide deployment to many scenarios. Recent advances reveal that a properly pre-trained model endows an important property: transferability. A higher transferability of the learned representations indicates a better generalizability across domains of different distributions (domain transferability), or across tasks of different semantics (task transferability). Transferability has become the key to enable data-efficient deep learning, however, existing pre-training methods focus only on domain transferability while meta-training methods only on task transferability. This restricts their data-efficiency in downstream scenarios of diverging domains and tasks. A finding of this paper is that even a tight combination of pre-training and meta-training cannot achieve both kinds of transferability. This motivates the proposed Omni-Training framework towards data-efficient deep learning. Our first contribution is Omni-Net, a tri-flow architecture. Besides the joint representation flow, Omni-Net introduces two new parallel flows for pre-training and meta-training, respectively responsible for learning representations of domain transferability and task transferability. Omni-Net coordinates the parallel flows by routing them via the joint-flow, making each gain the other kind of transferability. Our second contribution is Omni-Loss, in which a self-distillation regularization is imposed to enable knowledge transfer across the training process. Omni-Training is a general framework that accommodates many existing pre-training and meta-training algorithms. A thorough evaluation on cross-task and cross-domain datasets in classification, regression and reinforcement learning problems shows that Omni-Training consistently outperforms the state-of-the-art methods.

We study the problem of safe offline reinforcement learning (RL), the goal is to learn a policy that maximizes long-term reward while satisfying safety constraints given only offline data, without further interaction with the environment. This problem is more appealing for real world RL applications, in which data collection is costly or dangerous. Enforcing constraint satisfaction is non-trivial, especially in offline settings, as there is a potential large discrepancy between the policy distribution and the data distribution, causing errors in estimating the value of safety constraints. We show that na\"ive approaches that combine techniques from safe RL and offline RL can only learn sub-optimal solutions. We thus develop a simple yet effective algorithm, Constraints Penalized Q-Learning (CPQ), to solve the problem. Our method admits the use of data generated by mixed behavior policies. We present a theoretical analysis and demonstrate empirically that our approach can learn robustly across a variety of benchmark control tasks, outperforming several baselines.

Meta-learning models transfer the knowledge acquired from previous tasks to quickly learn new ones. They are trained on benchmarks with a fixed number of data points per task. This number is usually arbitrary and it is unknown how it affects performance at testing. Since labelling of data is expensive, finding the optimal allocation of labels across training tasks may reduce costs. Given a fixed budget of labels, should we use a small number of highly labelled tasks, or many tasks with few labels each? Should we allocate more labels to some tasks and less to others? We show that: 1) If tasks are homogeneous, there is a uniform optimal allocation, whereby all tasks get the same amount of data; 2) At fixed budget, there is a trade-off between number of tasks and number of data points per task, with a unique solution for the optimum; 3) When trained separately, harder task should get more data, at the cost of a smaller number of tasks; 4) When training on a mixture of easy and hard tasks, more data should be allocated to easy tasks. Interestingly, Neuroscience experiments have shown that human visual skills also transfer better from easy tasks. We prove these results mathematically on mixed linear regression, and we show empirically that the same results hold for few-shot image classification on CIFAR-FS and mini-ImageNet. Our results provide guidance for allocating labels across tasks when collecting data for meta-learning.

Machine Learning (ML) for Ligand Based Virtual Screening (LB-VS) is an important in-silico tool for discovering new drugs in a faster and cost-effective manner, especially for emerging diseases such as COVID-19. In this paper, we propose a general-purpose framework combining a classical Support Vector Classifier (SVC) algorithm with quantum kernel estimation for LB-VS on real-world databases, and we argue in favor of its prospective quantum advantage. Indeed, we heuristically prove that our quantum integrated workflow can, at least in some relevant instances, provide a tangible advantage compared to state-of-art classical algorithms operating on the same datasets, showing strong dependence on target and features selection method. Finally, we test our algorithm on IBM Quantum processors using ADRB2 and COVID-19 datasets, showing that hardware simulations provide results in line with the predicted performances and can surpass classical equivalents.

Political perspective detection has become an increasingly important task that can help combat echo chambers and political polarization. Previous approaches generally focus on leveraging textual content to identify stances, while they fail to reason with background knowledge or leverage the rich semantic and syntactic textual labels in news articles. In light of these limitations, we propose KCD, a political perspective detection approach to enable multi-hop knowledge reasoning and incorporate textual cues as paragraph-level labels. Specifically, we firstly generate random walks on external knowledge graphs and infuse them with news text representations. We then construct a heterogeneous information network to jointly model news content as well as semantic, syntactic and entity cues in news articles. Finally, we adopt relational graph neural networks for graph-level representation learning and conduct political perspective detection. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods on two benchmark datasets. We further examine the effect of knowledge walks and textual cues and how they contribute to our approach's data efficiency.

Given the trend of digitization and increasing number of maritime transport, prediction of vessel berth stay has been triggered for requirements of operation research and scheduling optimization problem in the era of maritime big data, which takes a significant part in port efficiency and maritime logistics enhancement. This study proposes a systematic and dynamic approach of predicting berth stay for tanker terminals. The approach covers three innovative aspects: 1) Data source employed is multi-faceted, including cargo operation data from tanker terminals, time-series data from automatic identification system (AIS), etc. 2) The process of berth stay is decomposed into multiple blocks according to data analysis and information extraction innovatively, and practical operation scenarios are also developed accordingly. 3) The predictive models of berth stay are developed on the basis of prior data analysis and information extraction under two methods, including regression and decomposed distribution. The models are evaluated under four dynamic scenarios with certain designated cargoes among two different terminals. The evaluation results show that the proposed approach can predict berth stay with the accuracy up to 98.81% validated by historical baselines, and also demonstrate the proposed approach has dynamic capability of predicting berth stay among the scenarios. The model may be potentially applied for short-term pilot-booking or scheduling optimizations within a reasonable time frame for advancement of port intelligence and logistics efficiency.

Federated learning protects data privacy and security by exchanging models instead of data. However, unbalanced data distributions among participating clients compromise the accuracy and convergence speed of federated learning algorithms. To alleviate this problem, unlike previous studies that limit the distance of updates for local models, we propose global-update-guided federated learning (FedGG), which introduces a model-cosine loss into local objective functions, so that local models can fit local data distributions under the guidance of update directions of global models. Furthermore, considering that the update direction of a global model is informative in the early stage of training, we propose adaptive loss weights based on the update distances of local models. Numerical simulations show that, compared with other advanced algorithms, FedGG has a significant improvement on model convergence accuracies and speeds. Additionally, compared with traditional fixed loss weights, adaptive loss weights enable our algorithm to be more stable and easier to implement in practice.

Ontology matching is a core task when creating interoperable and linked open datasets. In this paper, we explore a novel structure-based mapping approach which is based on knowledge graph embeddings: The ontologies to be matched are embedded, and an approach known as absolute orientation is used to align the two embedding spaces. Next to the approach, the paper presents a first, preliminary evaluation using synthetic and real-world datasets. We find in experiments with synthetic data, that the approach works very well on similarly structured graphs; it handles alignment noise better than size and structural differences in the ontologies.

Over the last few years, deep learning has grown in popularity for speaker verification, identification, and diarization. Inarguably, a significant part of this success is due to the demonstrated effectiveness of their speaker representations. These, however, are heavily dependent on large amounts of annotated data and can be sensitive to new domains. This study proposes an entirely unsupervised deep-learning model for speaker diarization. Specifically, the study focuses on generating high-quality neural speaker representations without any annotated data, as well as on estimating secondary hyperparameters of the model without annotations. The speaker embeddings are represented by an encoder trained in a self-supervised fashion using pairs of adjacent segments assumed to be of the same speaker. The trained encoder model is then used to self-generate pseudo-labels to subsequently train a similarity score between different segments of the same call using probabilistic linear discriminant analysis (PLDA) and further to learn a clustering stopping threshold. We compared our model to state-of-the-art unsupervised as well as supervised baselines on the CallHome benchmarks. According to empirical results, our approach outperforms unsupervised methods when only two speakers are present in the call, and is only slightly worse than recent supervised models.

Collaborative Inference (CI) optimizes the latency and energy consumption of deep learning inference through the inter-operation of edge and cloud devices. Albeit beneficial for other tasks, CI has never been applied to the sequence- to-sequence mapping problem at the heart of Neural Machine Translation (NMT). In this work, we address the specific issues of collaborative NMT, such as estimating the latency required to generate the (unknown) output sequence, and show how existing CI methods can be adapted to these applications. Our experiments show that CI can reduce the latency of NMT by up to 44% compared to a non-collaborative approach.

One intriguing property of adversarial attacks is their "transferability" -- an adversarial example crafted with respect to one deep neural network (DNN) model is often found effective against other DNNs as well. Intensive research has been conducted on this phenomenon under simplistic controlled conditions. Yet, thus far, there is still a lack of comprehensive understanding about transferability-based attacks ("transfer attacks") in real-world environments. To bridge this critical gap, we conduct the first large-scale systematic empirical study of transfer attacks against major cloud-based MLaaS platforms, taking the components of a real transfer attack into account. The study leads to a number of interesting findings which are inconsistent to the existing ones, including: (1) Simple surrogates do not necessarily improve real transfer attacks. (2) No dominant surrogate architecture is found in real transfer attacks. (3) It is the gap between posterior (output of the softmax layer) rather than the gap between logit (so-called $\kappa$ value) that increases transferability. Moreover, by comparing with prior works, we demonstrate that transfer attacks possess many previously unknown properties in real-world environments, such as (1) Model similarity is not a well-defined concept. (2) $L_2$ norm of perturbation can generate high transferability without usage of gradient and is a more powerful source than $L_\infty$ norm. We believe this work sheds light on the vulnerabilities of popular MLaaS platforms and points to a few promising research directions.

Due to the importance of Critical Infrastructure (CI) in a nation's economy, they have been lucrative targets for cyber attackers. These critical infrastructures are usually Cyber-Physical Systems (CPS) such as power grids, water, and sewage treatment facilities, oil and gas pipelines, etc. In recent times, these systems have suffered from cyber attacks numerous times. Researchers have been developing cyber security solutions for CIs to avoid lasting damages. According to standard frameworks, cyber security based on identification, protection, detection, response, and recovery are at the core of these research. Detection of an ongoing attack that escapes standard protection such as firewall, anti-virus, and host/network intrusion detection has gained importance as such attacks eventually affect the physical dynamics of the system. Therefore, anomaly detection in physical dynamics proves an effective means to implement defense-in-depth. PASAD is one example of anomaly detection in the sensor/actuator data, representing such systems' physical dynamics. We present EPASAD, which improves the detection technique used in PASAD to detect these micro-stealthy attacks, as our experiments show that PASAD's spherical boundary-based detection fails to detect. Our method EPASAD overcomes this by using Ellipsoid boundaries, thereby tightening the boundaries in various dimensions, whereas a spherical boundary treats all dimensions equally. We validate EPASAD using the dataset produced by the TE-process simulator and the C-town datasets. The results show that EPASAD improves PASAD's average recall by 5.8% and 9.5% for the two datasets, respectively.

In this study, a novel coordinative scheduling optimization approach is proposed to enhance port efficiency by reducing weighted average turnaround time. The proposed approach is developed as a heuristic algorithm applied and investigated through different observation windows with weekly rolling horizon paradigm method. The experimental results show that the proposed approach is effective and promising on mitigating the turnaround time of vessels. The results demonstrate that largest potential savings of turnaround time (weighted average) are around 17 hours (28%) reduction on baseline of 1-week observation, 45 hours (37%) reduction on baseline of 2-week observation and 70 hours (40%) reduction on baseline of 3-week observation. Even though the experimental results are based on historical datasets, the results potentially present significant benefits if real-time applications were applied under a quadratic computational complexity.

Various deep neural networks (DNNs) are developed and reported for their tremendous success in multiple domains. Given a specific task, developers can collect massive DNNs from public sources for efficient reusing and avoid redundant work from scratch. However, testing the performance (e.g., accuracy and robustness) of multiple DNNs and giving a reasonable recommendation that which model should be used is challenging regarding the scarcity of labeled data and demand of domain expertise. Existing testing approaches are mainly selection-based where after sampling, a few of the test data are labeled to discriminate DNNs. Therefore, due to the randomness of sampling, the performance ranking is not deterministic. In this paper, we propose a labeling-free comparison testing approach to overcome the limitations of labeling effort and sampling randomness. The main idea is to learn a Bayesian model to infer the models' specialty only based on predicted labels. To evaluate the effectiveness of our approach, we undertook exhaustive experiments on 9 benchmark datasets spanning in the domains of image, text, and source code, and 165 DNNs. In addition to accuracy, we consider the robustness against synthetic and natural distribution shifts. The experimental results demonstrate that the performance of existing approaches degrades under distribution shifts. Our approach outperforms the baseline methods by up to 0.74 and 0.53 on Spearman's correlation and Kendall's $\tau$, respectively, regardless of the dataset and distribution shift. Additionally, we investigated the impact of model quality (accuracy and robustness) and diversity (standard deviation of the quality) on the testing effectiveness and observe that there is a higher chance of a good result when the quality is over 50\% and the diversity is larger than 18\%.  ( 2 min )

Electrocardiograms (ECGs) have shown unique patterns to distinguish between different subjects and present important advantages compared to other biometric traits, such as difficulty to counterfeit, liveness detection, and ubiquity. Also, with the success of Deep Learning technologies, ECG biometric recognition has received increasing interest in recent years. However, it is not easy to evaluate the improvements of novel ECG proposed methods, mainly due to the lack of public data and standard experimental protocols. In this study, we perform extensive analysis and comparison of different scenarios in ECG biometric recognition. Both verification and identification tasks are investigated, as well as single- and multi-session scenarios. Finally, we also perform single- and multi-lead ECG experiments, considering traditional scenarios using electrodes in the chest and limbs and current user-friendly wearable devices. In addition, we present ECGXtractor, a robust Deep Learning technology trained with an in-house large-scale database and able to operate successfully across various scenarios and multiple databases. We introduce our proposed feature extractor, trained with multiple sinus-rhythm heartbeats belonging to 55,967 subjects, and provide a general public benchmark evaluation with detailed experimental protocol. We evaluate the system performance over four different databases: i) our in-house database, ii) PTB, iii) ECG-ID, and iv) CYBHi. With the widely used PTB database, we achieve Equal Error Rates of 0.14% and 2.06% in verification, and accuracies of 100% and 96.46% in identification, respectively in single- and multi-session analysis. We release the source code, experimental protocol details, and pre-trained models in GitHub to advance in the field.  ( 2 min )

As clean ImageNet accuracy nears its ceiling, the research community is increasingly more concerned about robust accuracy under distributional shifts. While a variety of methods have been proposed to robustify neural networks, these techniques often target models trained on ImageNet classification. At the same time, it is a common practice to use ImageNet pretrained backbones for downstream tasks such as object detection, semantic segmentation, and image classification from different domains. This raises a question: Can these robust image classifiers transfer robustness to downstream tasks? For object detection and semantic segmentation, we find that a vanilla Swin Transformer, a variant of Vision Transformer tailored for dense prediction tasks, transfers robustness better than Convolutional Neural Networks that are trained to be robust to the corrupted version of ImageNet. For CIFAR10 classification, we find that models that are robustified for ImageNet do not retain robustness when fully fine-tuned. These findings suggest that current robustification techniques tend to emphasize ImageNet evaluations. Moreover, network architecture is a strong source of robustness when we consider transfer learning.  ( 2 min )

Fashion is now among the largest industries worldwide, for it represents human history and helps tell the worlds story. As a result of the Fourth Industrial Revolution, the Internet has become an increasingly important source of fashion information. However, with a growing number of web pages and social data, it is nearly impossible for humans to manually catch up with the ongoing evolution and the continuously variable content in this domain. The proper management and exploitation of big data can pave the way for the substantial growth of the global economy as well as citizen satisfaction. Therefore, computer scientists have found it challenging to handle e-commerce fashion websites by using big data and machine learning technologies. This paper first proposes a scalable focused Web Crawler engine based on the distributed computing platforms to extract and process fashion data on e-commerce websites. The role of the proposed platform is then described in developing a disentangled feature extraction method by employing deep convolutional generative adversarial networks (DCGANs) for content-based image indexing and retrieval. Finally, the state-of-the-art solutions are compared, and the results of the proposed approach are analyzed on a standard dataset. For the real-life implementation of the proposed solution, a Web-based application is developed on Apache Storm, Kafka, Solr, and Milvus platforms to create a fashion search engine called SnapMode.  ( 2 min )

The traditional communication model based on chain of multiple independent processing blocks is constraint to efficiency and introduces artificial barriers. Thus, each individually optimized block does not guarantee end-to-end performance of the system. Recently, end-to-end learning of communications systems through machine learning (ML) have been proposed to optimize the system metrics jointly over all components. These methods show performance improvements but has a limitation that it requires a differentiable channel model. In this study, we have summarized the existing approaches that alleviates this problem. We believe that this study will provide better understanding of the topic and an insight into future research in this field.  ( 2 min )

The paper addresses acoustic vehicle detection and speed estimation from single sensor measurements. We predict the vehicle's pass-by instant by minimizing clipped vehicle-to-microphone distance, which is predicted from the mel-spectrogram of input audio, in a supervised learning approach. In addition, mel-spectrogram-based features are used directly for vehicle speed estimation, without introducing any intermediate features. The results show that the proposed features can be used for accurate vehicle detection and speed estimation, with an average error of 7.87 km/h. If we formulate speed estimation as a classification problem, with a 10 km/h discretization interval, the proposed method attains the average accuracy of 48.7% for correct class prediction and 91.0% when an offset of one class is allowed. The proposed method is evaluated on a dataset of 304 urban-environment on-field recordings of ten different vehicles.  ( 2 min )

We show that computing approximate stationary Markov coarse correlated equilibria (CCE) in general-sum stochastic games is computationally intractable, even when there are two players, the game is turn-based, the discount factor is an absolute constant, and the approximation is an absolute constant. Our intractability results stand in sharp contrast to normal-form games where exact CCEs are efficiently computable. A fortiori, our results imply that there are no efficient algorithms for learning stationary Markov CCE policies in multi-agent reinforcement learning (MARL), even when the interaction is two-player and turn-based, and both the discount factor and the desired approximation of the learned policies is an absolute constant. In turn, these results stand in sharp contrast to single-agent reinforcement learning (RL) where near-optimal stationary Markov policies can be efficiently learned. Complementing our intractability results for stationary Markov CCEs, we provide a decentralized algorithm (assuming shared randomness among players) for learning a nonstationary Markov CCE policy with polynomial time and sample complexity in all problem parameters. Previous work for learning Markov CCE policies all required exponential time and sample complexity in the number of players.  ( 2 min )

While most methods for solving mixed-integer optimization problems seek a single optimal solution, finding a diverse set of near-optimal solutions can often be more useful. State of the art methods for generating diverse near-optimal solutions usually take a two-phase approach, first finding a set of near-optimal solutions and then finding a diverse subset. In contrast, we present a method of finding a set of diverse solutions by emphasizing diversity within the search for near-optimal solutions. Specifically, within a branch-and-bound framework, we investigate parameterized node selection rules that explicitly consider diversity. Our results indicate that our approach significantly increases diversity of the final solution set. When compared with existing methods for finding diverse near-optimal sets, our method runs with similar run-time as regular node selection methods and gives a diversity improvement of up to 140%. In contrast, popular node selection rules such as best-first search gives an improvement of no more than 40%. Further, we find that our method is most effective when diversity is emphasized more in node selection when deeper in the tree and when the solution set has grown large enough.  ( 2 min )

In this study, a novel coordinative scheduling optimization approach is proposed to enhance port efficiency by reducing average wait time and turnaround time. The proposed approach consists of enhanced particle swarm optimization (ePSO) as kernel and augmented firefly algorithm (AFA) as global optimal search. Two paradigm methods of the proposed approach are investigated, which are batch method and rolling horizon method. The experimental results show that both paradigm methods of proposed approach can effectively enhance port efficiency. The average wait time could be significantly reduced by 86.0% - 95.5%, and the average turnaround time could eventually save 38.2% - 42.4% with respect to historical benchmarks. Moreover, the paradigm method of rolling horizon could reduce to 20 mins on running time over 3-month datasets, rather than 4 hrs on batch method at corresponding maximum performance.

Recently, it is shown that shuffling can amplify the central differential privacy guarantees of data randomized with local differential privacy. Within this setup, a centralized, trusted shuffler is responsible for shuffling by keeping the identities of data anonymous, which subsequently leads to stronger privacy guarantees for systems. However, introducing a centralized entity to the originally local privacy model loses some appeals of not having any centralized entity as in local differential privacy. Moreover, implementing a shuffler in a reliable way is not trivial due to known security issues and/or requirements of advanced hardware or secure computation technology. Motivated by these practical considerations, we rethink the shuffle model to relax the assumption of requiring a centralized, trusted shuffler. We introduce network shuffling, a decentralized mechanism where users exchange data in a random-walk fashion on a network/graph, as an alternative of achieving privacy amplification via anonymity. We analyze the threat model under such a setting, and propose distributed protocols of network shuffling that is straightforward to implement in practice. Furthermore, we show that the privacy amplification rate is similar to other privacy amplification techniques such as uniform shuffling. To our best knowledge, among the recently studied intermediate trust models that leverage privacy amplification techniques, our work is the first that is not relying on any centralized entity to achieve privacy amplification.  ( 2 min )

This Paper studies different committees of neural networks for biometric pattern recognition. We use the neural nets as classifiers for identification and verification purposes. We show that a committee of nets can improve the recognition rates when compared with a multi-start initialization algo-rithm that just picks up the neural net which offers the best performance. On the other hand, we found that there is no strong correlation between identifi-cation and verification applications using the same classifier.  ( 2 min )

Literature on machine learning for multiple sclerosis has primarily focused on the use of neuroimaging data such as magnetic resonance imaging and clinical laboratory tests for disease identification. However, studies have shown that these modalities are not consistent with disease activity such as symptoms or disease progression. Furthermore, the cost of collecting data from these modalities is high, leading to scarce evaluations. In this work, we used multi-dimensional, affordable, physical and smartphone-based performance outcome measures (POM) in conjunction with demographic data to predict multiple sclerosis disease progression. We performed a rigorous benchmarking exercise on two datasets and present results across 13 clinically actionable prediction endpoints and 6 machine learning models. To the best of our knowledge, our results are the first to show that it is possible to predict disease progression using POMs and demographic data in the context of both clinical trials and smartphone-base studies by using two datasets. Moreover, we investigate our models to understand the impact of different POMs and demographics on model performance through feature ablation studies. We also show that model performance is similar across different demographic subgroups (based on age and sex). To enable this work, we developed an end-to-end reusable pre-processing and machine learning framework which allows quicker experimentation over disparate MS datasets.  ( 2 min )

In a recent work, we presented a novel state-of-the-art approach to zero-shot slot filling that extends dense passage retrieval with hard negatives and robust training procedures for retrieval augmented generation models. In this paper, we propose a system based on an enhanced version of this approach where we train task specific models for other knowledge intensive language tasks, such as open domain question answering (QA), dialogue and fact checking. Our system achieves results comparable to the best models in the KILT leaderboards. Moreover, given a user query, we show how the output from these different models can be combined to cross-examine each other. Particularly, we show how accuracy in dialogue can be improved using the QA model. A short video demonstrating the system is available here - \url{https://ibm.box.com/v/kgi-interactive-demo} .  ( 2 min )

Due to the cost or interference of measurement, we need to control measurement system. Assuming that each variable can be measured sequentially, there exists optimal policy choosing next measurement for the former observations. Though optimal measurement policy is actually dependent on the goal of measurement, we mainly focus on retrieving complete data, so called as imputation. Also, we adapt the imputation method to missingness varying with measurement policy. However, learning measurement policy and imputation requires complete data which is impossible to be observed, unfortunately. To tackle this problem, we propose a data generation method and joint learning algorithm. The main idea is that 1) the data generation method is inherited by imputation method, and 2) the adaptation of imputation encourages measurement policy to learn more than individual learning. We implemented some variations of proposed algorithm for two different datasets and various missing rates. From the experimental results, we demonstrate that our algorithm is generally applicable and outperforms baseline methods.  ( 2 min )

Federated learning (FL) is a distributed learning paradigm that enables multiple clients to collaboratively learn a shared global model. Despite the recent progress, it remains challenging to deal with heterogeneous data clients, as the discrepant data distributions usually prevent the global model from delivering good generalization ability on each participating client. In this paper, we propose CD^2-pFed, a novel Cyclic Distillation-guided Channel Decoupling framework, to personalize the global model in FL, under various settings of data heterogeneity. Different from previous works which establish layer-wise personalization to overcome the non-IID data across different clients, we make the first attempt at channel-wise assignment for model personalization, referred to as channel decoupling. To further facilitate the collaboration between private and shared weights, we propose a novel cyclic distillation scheme to impose a consistent regularization between the local and global model representations during the federation. Guided by the cyclical distillation, our channel decoupling framework can deliver more accurate and generalized results for different kinds of heterogeneity, such as feature skew, label distribution skew, and concept shift. Comprehensive experiments on four benchmarks, including natural image and medical image analysis tasks, demonstrate the consistent effectiveness of our method on both local and external validations.  ( 2 min )

Speech intelligibility assessment plays an important role in the therapy of patients suffering from pathological speech disorders. Automatic and objective measures are desirable to assist therapists in their traditionally subjective and labor-intensive assessments. In this work, we investigate a novel approach for obtaining such a measure using the divergence in disentangled latent speech representations of a parallel utterance pair, obtained from a healthy reference and a pathological speaker. Experiments on an English database of Cerebral Palsy patients, using all available utterances per speaker, show high and significant correlation values (R = -0.9) with subjective intelligibility measures, while having only minimal deviation (+-0.01) across four different reference speaker pairs. We also demonstrate the robustness of the proposed method (R = -0.89 deviating +-0.02 over 1000 iterations) by considering a significantly smaller amount of utterances per speaker. Our results are among the first to show that disentangled speech representations can be used for automatic pathological speech intelligibility assessment, resulting in a reference speaker pair invariant method, applicable in scenarios with only few utterances available.  ( 2 min )

The modeling of jet substructure significantly differs between Parton Shower Monte Carlo (PSMC) programs. Despite this, we observe that machine learning classifiers trained on different PSMCs learn nearly the same function. This means that when these classifiers are applied to the same PSMC for testing, they result in nearly the same performance. This classifier universality indicates that a machine learning model trained on one simulation and tested on another simulation (or data) will likely be optimal. Our observations are based on detailed studies of shallow and deep neural networks applied to simulated Lorentz boosted Higgs jet tagging at the LHC.  ( 2 min )

Deep Neural Networks (DNN) have made significant progress in a wide range of visual recognition tasks such as image classification, object detection, and semantic segmentation. The evolution of convolutional architectures has led to better performance by incurring expensive computational costs. In addition, network design has become a difficult task, which is labor-intensive and requires a high level of domain knowledge. To mitigate such issues, there have been studies for a variety of neural architecture search methods that automatically search for optimal architectures, achieving models with impressive performance that outperform human-designed counterparts. This survey aims to provide an overview of existing works in this field of research and specifically focus on the supernet optimization that builds a neural network that assembles all the architectures as its sub models by using weight sharing. We aim to accomplish that by categorizing supernet optimization by proposing them as solutions to the common challenges found in the literature: data-side optimization, poor rank correlation alleviation, and transferable NAS for a number of deployment scenarios.  ( 2 min )

Training action space selection for reinforcement learning (RL) is conflict-prone due to complex state-action relationships. To address this challenge, this paper proposes a Shapley-inspired methodology for training action space categorization and ranking. To reduce exponential-time shapley computations, the methodology includes a Monte Carlo simulation to avoid unnecessary explorations. The effectiveness of the methodology is illustrated using a cloud infrastructure resource tuning case study. It reduces the search space by 80\% and categorizes the training action sets into dispensable and indispensable groups. Additionally, it ranks different training actions to facilitate high-performance yet cost-efficient RL model design. The proposed data-driven methodology is extensible to different domains, use cases, and reinforcement learning algorithms.  ( 2 min )

Recognition of user interaction, in particular engagement detection, became highly crucial for online working and learning environments, especially during the COVID-19 outbreak. Such recognition and detection systems significantly improve the user experience and efficiency by providing valuable feedback. In this paper, we propose a novel Engagement Detection with Multi-Task Training (ED-MTT) system which minimizes mean squared error and triplet loss together to determine the engagement level of students in an e-learning environment. The performance of this system is evaluated and compared against the state-of-the-art on a publicly available dataset as well as videos collected from real-life scenarios. The results show that ED-MTT achieves 6% lower MSE than the best state-of-the-art performance with highly acceptable training time and lightweight feature extraction.

The knowledge, embodied in machine learning models for intelligent systems, is commonly associated with time-consuming and costly processes such as large-scale data collection, data labelling, network training, and fine-tuning of models. Sharing and reuse of these elaborated models between intelligent systems deployed in a different environment, which is known as transfer learning, would facilitate the adoption of services for the users and accelerates the uptake of intelligent systems in environments such as smart building and smart city applications. In this context, the communication and knowledge exchange between AI-enabled environments depend on a complicated networks of systems, system of systems, digital assets, and their chain of dependencies that hardly follows the centralized schema of traditional information systems. Rather, it requires an adaptive decentralized system architecture that is empowered by features such as data provenance, workflow transparency, and validation of process participants. In this research, we propose a decentralized and adaptive software framework based on blockchain and knowledge graph technologies that supports the knowledge exchange and interoperability between IoT-enabled environments, in a transparent and trustworthy way.  ( 2 min )

The field of bibliometrics, studying citations and behavior, is critical to the discussion of reproducibility. Citations are one of the primary incentive and reward systems for academic work, and so we desire to know if this incentive rewards reproducible work. Yet to the best of our knowledge, only one work has attempted to look at this combined space, concluding that non-reproducible work is more highly cited. We show that answering this question is more challenging than first proposed, and subtle issues can inhibit a robust conclusion. To make inferences with more robust behavior, we propose a hierarchical Bayesian model that incorporates the citation rate over time, rather than the total number of citations after a fixed amount of time. In doing so we show that, under current evidence the answer is more likely that certain fields of study such as Medicine and Machine Learning (ML) do correlate reproducible works with more citations, but other fields appear to have no relationship. Further, we find that making code available and thoroughly referencing prior works appear to also positively correlate with increased citations. Our code and data can be found at https://github.com/EdwardRaff/ReproducibleCitations .  ( 2 min )

The use of machine learning to build subgrid parametrizations for climate models is receiving growing attention. State-of-the-art strategies address the problem as a supervised learning task and optimize algorithms that predict subgrid fluxes based on information from coarse resolution models. In practice, training data are generated from higher resolution numerical simulations transformed in order to mimic coarse resolution simulations. By essence, these strategies optimize subgrid parametrizations to meet so-called $\textit{a priori}$ criteria. But the actual purpose of a subgrid parametrization is to obtain good performance in terms of $\textit{a posteriori}$ metrics which imply computing entire model trajectories. In this paper, we focus on the representation of energy backscatter in two dimensional quasi-geostrophic turbulence and compare parametrizations obtained with different learning strategies at fixed computational complexity. We show that strategies based on $\textit{a priori}$ criteria yield parametrizations that tend to be unstable in direct simulations and describe how subgrid parametrizations can alternatively be trained end-to-end in order to meet $\textit{a posteriori}$ criteria. We illustrate that end-to-end learning strategies yield parametrizations that outperform known empirical and data-driven schemes in terms of performance, stability and ability to apply to different flow configurations. These results support the relevance of differentiable programming paradigms for climate models in the future.  ( 2 min )

We consider two federated learning algorithms for training partially personalized models, where the shared and personal parameters are updated either simultaneously or alternately on the devices. Both algorithms have been proposed in the literature, but their convergence properties are not fully understood, especially for the alternating variant. We provide convergence analyses of both algorithms in the general nonconvex setting with partial participation and delineate the regime where one dominates the other. Our experiments on real-world image, text, and speech datasets demonstrate that (a) partial personalization can obtain most of the benefits of full model personalization with a small fraction of personal parameters, and, (b) the alternating update algorithm often outperforms the simultaneous update algorithm.  ( 2 min )

Partial label learning (PLL) is a typical weakly supervised learning problem, where each training example is associated with a set of candidate labels among which only one is true. Most existing PLL approaches assume that the incorrect labels in each training example are randomly picked as the candidate labels and model the generation process of the candidate labels in a simple way. However, these approaches usually do not perform as well as expected due to the fact that the generation process of the candidate labels is always instance-dependent. Therefore, it deserves to be modeled in a refined way. In this paper, we consider instance-dependent PLL and assume that the generation process of the candidate labels could decompose into two sequential parts, where the correct label emerges first in the mind of the annotator but then the incorrect labels related to the feature are also selected with the correct label as candidate labels due to uncertainty of labeling. Motivated by this consideration, we propose a novel PLL method that performs Maximum A Posterior(MAP) based on an explicitly modeled generation process of candidate labels via decomposed probability distribution models. Experiments on benchmark and real-world datasets validate the effectiveness of the proposed method.  ( 2 min )

$Q$-learning is the most fundamental model-free reinforcement learning algorithm. Deployment of $Q$-learning requires approximation of the state-action value function (also known as the $Q$-function). In this work, we provide online random forests as $Q$-function approximators and propose a novel method wherein the random forest is grown as learning proceeds (through expanding forests). We demonstrate improved performance of our methods over state-of-the-art Deep $Q$-Networks in two OpenAI gyms (`blackjack' and `inverted pendulum') but not in the `lunar lander' gym. We suspect that the resilience to overfitting enjoyed by random forests recommends our method for common tasks that do not require a strong representation of the problem domain. We show that expanding forests (in which the number of trees increases as data comes in) improve performance, suggesting that expanding forests are viable for other applications of online random forests beyond the reinforcement learning setting.  ( 2 min )

In this work we introduce a quantum sorting algorithm with adaptable requirements of memory and circuit depth, and then use it to develop a new quantum version of the classical machine learning algorithm known as k-nearest neighbors (k-NN). Both the efficiency and performance of this new quantum version of the k-NN algorithm are compared to those of the classical k-NN and another quantum version proposed by Schuld et al. \cite{Int13}. Results show that the efficiency of both quantum algorithms is similar to each other and superior to that of the classical algorithm. On the other hand, the performance of our proposed quantum k-NN algorithm is superior to the one proposed by Schuld et al. and similar to that of the classical k-NN.  ( 2 min )

Personalization of on-device speech recognition (ASR) has seen explosive growth in recent years, largely due to the increasing popularity of personal assistant features on mobile devices and smart home speakers. In this work, we present Personal VAD 2.0, a personalized voice activity detector that detects the voice activity of a target speaker, as part of a streaming on-device ASR system. Although previous proof-of-concept studies have validated the effectiveness of Personal VAD, there are still several critical challenges to address before this model can be used in production: first, the quality must be satisfactory in both enrollment and enrollment-less scenarios; second, it should operate in a streaming fashion; and finally, the model size should be small enough to fit a limited latency and CPU/Memory budget. To meet the multi-faceted requirements, we propose a series of novel designs: 1) advanced speaker embedding modulation methods; 2) a new training paradigm to generalize to enrollment-less conditions; 3) architecture and runtime optimizations for latency and resource restrictions. Extensive experiments on a realistic speech recognition system demonstrated the state-of-the-art performance of our proposed method.  ( 2 min )

The evaluation of the free energy of a stochastic model is considered to be a significant issue in various fields of physics and machine learning. However, the exact free energy evaluation is computationally infeasible because it includes an intractable partition function. Annealed importance sampling (AIS) is a type of importance sampling based on the Markov chain Monte Carlo method, which is similar to a simulated annealing, and can effectively approximate the free energy. This study proposes a new AIS-based approach, referred to as marginalized AIS (mAIS). The statistical efficiency of mAIS is investigated in detail based on a theoretical and numerical perspectives. Based on the investigation, it has been proved that mAIS is more effective than AIS under a certain condition.  ( 2 min )

Generating multi-contrasts/modal MRI of the same anatomy enriches diagnostic information but is limited in practice due to excessive data acquisition time. In this paper, we propose a novel deep-learning model for joint reconstruction and synthesis of multi-modal MRI using incomplete k-space data of several source modalities as inputs. The output of our model includes reconstructed images of the source modalities and high-quality image synthesized in the target modality. Our proposed model is formulated as a variational problem that leverages several learnable modality-specific feature extractors and a multimodal synthesis module. We propose a learnable optimization algorithm to solve this model, which induces a multi-phase network whose parameters can be trained using multi-modal MRI data. Moreover, a bilevel-optimization framework is employed for robust parameter training. We demonstrate the effectiveness of our approach using extensive numerical experiments.  ( 2 min )

Class imbalance poses new challenges when it comes to classifying data streams. Many algorithms recently proposed in the literature tackle this problem using a variety of data-level, algorithm-level, and ensemble approaches. However, there is a lack of standardized and agreed-upon procedures on how to evaluate these algorithms. This work presents a taxonomy of algorithms for imbalanced data streams and proposes a standardized, exhaustive, and informative experimental testbed to evaluate algorithms in a collection of diverse and challenging imbalanced data stream scenarios. The experimental study evaluates 24 state-of-the-art data streams algorithms on 515 imbalanced data streams that combine static and dynamic class imbalance ratios, instance-level difficulties, concept drift, real-world and semi-synthetic datasets in binary and multi-class scenarios. This leads to the largest experimental study conducted so far in the data stream mining domain. We discuss the advantages and disadvantages of state-of-the-art classifiers in each of these scenarios and we provide general recommendations to end-users for selecting the best algorithms for imbalanced data streams. Additionally, we formulate open challenges and future directions for this domain. Our experimental testbed is fully reproducible and easy to extend with new methods. This way we propose the first standardized approach to conducting experiments in imbalanced data streams that can be used by other researchers to create trustworthy and fair evaluation of newly proposed methods. Our experimental framework can be downloaded from https://github.com/canoalberto/imbalanced-streams.  ( 2 min )

A recent emphasis of distributed learning research has been on federated learning (FL), in which model training is conducted by the data-collecting devices. Existing research on FL has mostly focused on a star topology learning architecture with synchronized (time-triggered) model training rounds, where the local models of the devices are periodically aggregated by a centralized coordinating node. However, in many settings, such a coordinating node may not exist, motivating efforts to fully decentralize FL. In this work, we propose a novel methodology for distributed model aggregations via asynchronous, event-triggered consensus iterations over the network graph topology. We consider heterogeneous communication event thresholds at each device that weigh the change in local model parameters against the available local resources in deciding the benefit of aggregations at each iteration. Through theoretical analysis, we demonstrate that our methodology achieves asymptotic convergence to the globally optimal learning model under standard assumptions in distributed learning and graph consensus literature, and without restrictive connectivity requirements on the underlying topology. Subsequent numerical results demonstrate that our methodology obtains substantial improvements in communication requirements compared with FL baselines.  ( 2 min )

Objective: Global (inter-patient) ECG classification for arrhythmia detection over Electrocardiogram (ECG) signal is a challenging task for both humans and machines. The main reason is the significant variations of both normal and arrhythmic ECG patterns among patients. Automating this process with utmost accuracy is, therefore, highly desirable due to the advent of wearable ECG sensors. However, even with numerous deep learning approaches proposed recently, there is still a notable gap in the performance of global and patient-specific ECG classification performances. This study proposes a novel approach to narrow this gap and propose a real-time solution with shallow and compact 1D Self-Organized Operational Neural Networks (Self-ONNs). Methods: In this study, we propose a novel approach for inter-patient ECG classification using a compact 1D Self-ONN by exploiting morphological and timing information in heart cycles. We used 1D Self-ONN layers to automatically learn morphological representations from ECG data, enabling us to capture the shape of the ECG waveform around the R peaks. We further inject temporal features based on RR interval for timing characterization. The classification layers can thus benefit from both temporal and learned features for the final arrhythmia classification. Results: Using the MIT-BIH arrhythmia benchmark database, the proposed method achieves the highest classification performance ever achieved, i.e., 99.21% precision, 99.10% recall, and 99.15% F1-score for normal (N) segments; 82.19% precision, 82.50% recall, and 82.34% F1-score for the supra-ventricular ectopic beat (SVEBs); and finally, 94.41% precision, 96.10% recall, and 95.2% F1-score for the ventricular-ectopic beats (VEBs).  ( 2 min )

Despite recent improvements in computer vision, artificial visual systems' design is still daunting since an explanation of visual computing algorithms remains elusive. Salient object detection is one problem that is still open due to the difficulty of understanding the brain's inner workings. Progress on this research area follows the traditional path of hand-made designs using neuroscience knowledge. In recent years two different approaches based on genetic programming appear to enhance their technique. One follows the idea of combining previous hand-made methods through genetic programming and fuzzy logic. The other approach consists of improving the inner computational structures of basic hand-made models through artificial evolution. This research work proposes expanding the artificial dorsal stream using a recent proposal to solve salient object detection problems. This approach uses the benefits of the two main aspects of this research area: fixation prediction and detection of salient objects. We decided to apply the fusion of visual saliency and image segmentation algorithms as a template. The proposed methodology discovers several critical structures in the template through artificial evolution. We present results on a benchmark designed by experts with outstanding results in comparison with the state-of-the-art.  ( 2 min )

When writing programs, people have the ability to tackle a new complex task by decomposing it into smaller and more familiar subtasks. While it is difficult to measure whether neural program synthesis methods have similar capabilities, what we can measure is whether they compositionally generalize, that is, whether a model that has been trained on the simpler subtasks is subsequently able to solve more complex tasks. In this paper, we focus on measuring the ability of learned program synthesizers to compositionally generalize. We first characterize several different axes along which program synthesis methods would be desired to generalize, e.g., length generalization, or the ability to combine known subroutines in new ways that do not occur in the training data. Based on this characterization, we introduce a benchmark suite of tasks to assess these abilities based on two popular existing datasets, SCAN and RobustFill. Finally, we make first attempts to improve the compositional generalization ability of Transformer models along these axes through novel attention mechanisms that draw inspiration from a human-like decomposition strategy. Empirically, we find our modified Transformer models generally perform better than natural baselines, but the tasks remain challenging.  ( 2 min )

X-ray tomography is capable of imaging the interior of objects in three dimensions non-invasively, with applications in biomedical imaging, materials science, electronic inspection, and other fields. The reconstruction process can be an ill-conditioned inverse problem, requiring regularization to obtain satisfactory reconstructions. Recently, deep learning has been adopted for tomographic reconstruction. Unlike iterative algorithms which require a distribution that is known a priori, deep reconstruction networks can learn a prior distribution through sampling the training distributions. In this work, we develop a Physics-assisted Generative Adversarial Network (PGAN), a two-step algorithm for tomographic reconstruction. In contrast to previous efforts, our PGAN utilizes maximum-likelihood estimates derived from the measurements to regularize the reconstruction with both known physics and the learned prior. Synthetic objects with spatial correlations are integrated circuits (IC) from a proposed model CircuitFaker. Compared with maximum-likelihood estimation, PGAN can reduce the photon requirement with limited projection angles to achieve a given error rate. We further attribute the improvement to the learned prior by reconstructing objects created without spatial correlations. The advantages of using a prior from deep learning in X-ray tomography may further enable low-photon nanoscale imaging.  ( 2 min )

Digital harms can manifest across any interface. Key problems in addressing these harms include the high individuality of harms and the fast-changing nature of digital systems. As a result, we still lack a systematic approach to study harms and produce interventions for end-users. We put forward GreaseVision, a new framework that enables end-users to collaboratively develop interventions against harms in software using a no-code approach and recent advances in few-shot machine learning. The contribution of the framework and tool allow individual end-users to study their usage history and create personalized interventions. Our contribution also enables researchers to study the distribution of harms and interventions at scale.  ( 2 min )

With the rapid development of deep learning, automatic modulation recognition (AMR), as an important task in cognitive radio, has gradually transformed from traditional feature extraction and classification to automatic classification by deep learning technology. However, deep learning models are data-driven methods, which often require a large amount of data as the training support. Data augmentation, as the strategy of expanding dataset, can improve the generalization of the deep learning models and thus improve the accuracy of the models to a certain extent. In this paper, for AMR of radio signals, we propose a data augmentation strategy based on mixing signals and consider four specific methods (Random Mixing, Maximum-Similarity-Mixing, $\theta-$Similarity Mixing and n-times Random Mixing) to achieve data augmentation. Experiments show that our proposed method can improve the classification accuracy of deep learning based AMR models in the full public dataset RML2016.10a. In particular, for the case of a single signal-to-noise ratio signal set, the classification accuracy can be significantly improved, which verifies the effectiveness of the methods.  ( 2 min )

This paper presents a nonlinear location estimation to infer the position of a user holding a smartphone. We consider a large location with $M$ number of grid points, each grid point is labeled with a unique fingerprint consisting of the received signal strength (RSS) values measured from $N$ number of Bluetooth Low Energy (BLE) beacons. Given the fingerprint observed by the smartphone, the user's current location can be estimated by finding the top-k similar fingerprints from the list of fingerprints registered in the database. Besides the environmental factors, the dynamicity in holding the smartphone is another source to the variation in fingerprint measurements, yet there are not many studies addressing the fingerprint variability due to dynamic smartphone positions held by human hands during online detection. To this end, we propose a nonlinear location estimation using the kernel method. Specifically, our proposed method comprises of two steps: 1) a beacon selection strategy to select a subset of beacons that is insensitive to the subtle change of holding positions, and 2) a kernel method to compute the similarity between this subset of observed signals and all the fingerprints registered in the database. The experimental results based on large-scale data collected in a complex building indicate a substantial performance gain of our proposed approach in comparison to state-of-the-art methods. The dataset consisting of the signal information collected from the beacons is available online.  ( 2 min )

Recommender Systems use the user's profile to generate a recommendation list with unknown items to a target user. Although the primary goal of traditional recommendation systems is to deliver the most relevant items, such an effort unintentionally can cause collateral effects including low diversity and unbalanced genres or categories, benefiting particular groups of categories. This paper proposes an approach to create recommendation lists with a calibrated balance of genres, avoiding disproportion between the user's profile interests and the recommendation list. The calibrated recommendations consider concomitantly the relevance and the divergence between the genres distributions extracted from the user's preference and the recommendation list. The main claim is that calibration can contribute positively to generate fairer recommendations. In particular, we propose a new trade-off equation, which considers the users' bias to provide a recommendation list that seeks for the users' tendencies. Moreover, we propose a conceptual framework and a decision protocol to generate more than one thousand combinations of calibrated systems in order to find the best combination. We compare our approach against state-of-the-art approaches using multiple domain datasets, which are analyzed by rank and calibration metrics. The results indicate that the trade-off, which considers the users' bias, produces positive effects on the precision and to the fairness, thus generating recommendation lists that respect the genre distribution and, through the decision protocol, we also found the best system for each dataset.  ( 2 min )

We propose a novel approach to generate temporally coherent UV coordinates for loose clothing. Our method is not constrained by human body outlines and can capture loose garments and hair. We implemented a differentiable pipeline to learn UV mapping between a sequence of RGB inputs and textures via UV coordinates. Instead of treating the UV coordinates of each frame separately, our data generation approach connects all UV coordinates via feature matching for temporal stability. Subsequently, a generative model is trained to balance the spatial quality and temporal stability. It is driven by supervised and unsupervised losses in both UV and image spaces. Our experiments show that the trained models output high-quality UV coordinates and generalize to new poses. Once a sequence of UV coordinates has been inferred by our model, it can be used to flexibly synthesize new looks and modified visual styles. Compared to existing methods, our approach reduces the computational workload to animate new outfits by several orders of magnitude.  ( 2 min )

Deep learning and big data algorithms have become widely used in industrial applications to optimize several tasks in many complex systems. Particularly, deep learning model for diagnosing and prognosing machinery health has leveraged predictive maintenance (PdM) to be more accurate and reliable in decision making, in this way avoiding unnecessary interventions, machinery accidents, and environment catastrophes. Recently, Transformer Neural Networks have gained notoriety and have been increasingly the favorite choice for Natural Language Processing (NLP) tasks. Thus, given their recent major achievements in NLP, this paper proposes the development of an automatic fault classifier model for predictive maintenance based on a modified version of the Transformer architecture, namely T4PdM, to identify multiple types of faults in rotating machinery. Experimental results are developed and presented for the MaFaulDa and CWRU databases. T4PdM was able to achieve an overall accuracy of 99.98% and 98% for both datasets, respectively. In addition, the performance of the proposed model is compared to other previously published works. It has demonstrated the superiority of the model in detecting and classifying faults in rotating industrial machinery. Therefore, the proposed Transformer-based model can improve the performance of machinery fault analysis and diagnostic processes and leverage companies to a new era of the Industry 4.0. In addition, this methodology can be adapted to any other task of time series classification.  ( 2 min )

Brain-inspired hyperdimensional computing (HDC) is an emerging machine learning (ML) methods. It is based on large vectors of binary or bipolar symbols and a few simple mathematical operations. The promise of HDC is a highly efficient implementation for embedded systems like wearables. While fast implementations have been presented, other constraints have not been considered for edge computing. In this work, we aim at answering how thermal-friendly HDC for edge computing is. Devices like smartwatches, smart glasses, or even mobile systems have a restrictive cooling budget due to their limited volume. Although HDC operations are simple, the vectors are large, resulting in a high number of CPU operations and thus a heavy load on the entire system potentially causing temperature violations. In this work, the impact of HDC on the chip's temperature is investigated for the first time. We measure the temperature and power consumption of a commercial embedded system and compare HDC with conventional CNN. We reveal that HDC causes up to 6.8{\deg}C higher temperatures and leads to up to 47% more CPU throttling. Even when both HDC and CNN aim for the same throughput (i.e., perform a similar number of classifications per second), HDC still causes higher on-chip temperatures due to the larger power consumption.  ( 2 min )

Deep networks achieve state-of-the-art performance on computer vision tasks, yet they fail under adversarial attacks that are imperceptible to humans. In this paper, we propose a novel defense that can dynamically adapt the input using the intrinsic structure from multiple self-supervised tasks. By simultaneously using many self-supervised tasks, our defense avoids over-fitting the adapted image to one specific self-supervised task and restores more intrinsic structure in the image compared to a single self-supervised task approach. Our approach further improves robustness and clean accuracy significantly compared to the state-of-the-art single task self-supervised defense. Our work is the first to connect multiple self-supervised tasks to robustness, and suggests that we can achieve better robustness with more intrinsic signal from visual data.  ( 2 min )

Millions of people around the world have low or no vision. Assistive software applications have been developed for a variety of day-to-day tasks, including optical character recognition, scene identification, person recognition, and currency recognition. This last task, the recognition of banknotes from different denominations, has been addressed by the use of computer vision models for image recognition. However, the datasets and models available for this task are limited, both in terms of dataset size and in variety of currencies covered. In this work, we collect a total of 24,826 images of banknotes in variety of assistive settings, spanning 17 currencies and 112 denominations. Using supervised contrastive learning, we develop a machine learning model for universal currency recognition. This model learns compliant embeddings of banknote images in a variety of contexts, which can be shared publicly (as a compressed vector representation), and can be used to train and test specialized downstream models for any currency, including those not covered by our dataset or for which only a few real images per denomination are available (few-shot learning). We deploy a variation of this model for public use in the last version of the Seeing AI app developed by Microsoft. We share our encoder model and the embeddings as an open dataset in our BankNote-Net repository.  ( 2 min )

Quality-Diversity (QD) algorithms can discover large and complex behavioural repertoires consisting of both diverse and high-performing skills. However, the generation of behavioural repertoires has mainly been limited to simulation environments instead of real-world learning. This is because existing QD algorithms need large numbers of evaluations as well as episodic resets, which require manual human supervision and interventions. This paper proposes Reset-Free Quality-Diversity optimization (RF-QD) as a step towards autonomous learning for robotics in open-ended environments. We build on Dynamics-Aware Quality-Diversity (DA-QD) and introduce a behaviour selection policy that leverages the diversity of the imagined repertoire and environmental information to intelligently select of behaviours that can act as automatic resets. We demonstrate this through a task of learning to walk within defined training zones with obstacles. Our experiments show that we can learn full repertoires of legged locomotion controllers autonomously without manual resets with high sample efficiency in spite of harsh safety constraints. Finally, using an ablation of different target objectives, we show that it is important for RF-QD to have diverse types solutions available for the behaviour selection policy over solutions optimised with a specific objective. Videos and code available at https://sites.google.com/view/rf-qd.  ( 2 min )

The development of noninvasive brain imaging such as resting-state functional magnetic resonance imaging (rs-fMRI) and its combination with AI algorithm provides a promising solution for the early diagnosis of Autism spectrum disorder (ASD). However, the performance of the current ASD classification based on rs-fMRI still needs to be improved. This paper introduces a classification framework to aid ASD diagnosis based on rs-fMRI. In the framework, we proposed a novel filter feature selection method based on the difference between step distribution curves (DSDC) to select remarkable functional connectivities (FCs) and utilized a multilayer perceptron (MLP) which was pretrained by a simplified Variational Autoencoder (VAE) for classification. We also designed a pipeline consisting of a normalization procedure and a modified hyperbolic tangent (tanh) activation function to replace the original tanh function, further improving the model accuracy. Our model was evaluated by 10 times 10-fold cross-validation and achieved an average accuracy of 78.12%, outperforming the state-of-the-art methods reported on the same dataset. Given the importance of sensitivity and specificity in disease diagnosis, two constraints were designed in our model which can improve the model's sensitivity and specificity by up to 9.32% and 10.21%, respectively. The added constraints allow our model to handle different application scenarios and can be used broadly.  ( 2 min )

We present a general method, called Qade, for solving differential equations using a quantum annealer. The solution is obtained as a linear combination of a set of basis functions. On current devices, Qade can solve systems of coupled partial differential equations that depend linearly on the solution and its derivatives, with non-linear variable coefficients and arbitrary inhomogeneous terms. We test the method with several examples and find that state-of-the-art quantum annealers can find the solution accurately for problems requiring a small enough function basis. We provide a Python package implementing the method at gitlab.com/jccriado/qade.  ( 2 min )

This paper presents a novel model training solution, denoted as Adaptive-Gravity, for enhancing the robustness of deep neural network classifiers against adversarial examples. We conceptualize the model parameters/features associated with each class as a mass characterized by its centroid location and the spread (standard deviation of the distance) of features around the centroid. We use the centroid associated with each cluster to derive an anti-gravity force that pushes the centroids of different classes away from one another during network training. Then we customized an objective function that aims to concentrate each class's features toward their corresponding new centroid, which has been obtained by anti-gravity force. This methodology results in a larger separation between different masses and reduces the spread of features around each centroid. As a result, the samples are pushed away from the space that adversarial examples could be mapped to, effectively increasing the degree of perturbation needed for making an adversarial example. We have implemented this training solution as an iterative method consisting of four steps at each iteration: 1) centroid extraction, 2) anti-gravity force calculation, 3) centroid relocation, and 4) gravity training. Gravity's efficiency is evaluated by measuring the corresponding fooling rates against various attack models, including FGSM, MIM, BIM, and PGD using LeNet and ResNet110 networks, benchmarked against MNIST and CIFAR10 classification problems. Test results show that Gravity not only functions as a powerful instrument to robustify a model against state-of-the-art adversarial attacks but also effectively improves the model training accuracy.  ( 2 min )

Simulation-based inference enables learning the parameters of a model even when its likelihood cannot be computed in practice. One class of methods uses data simulated with different parameters to infer models of the likelihood-to-evidence ratio, or equivalently the posterior function. Here we frame the inference task as an estimation of an energy function parametrized with an artificial neural network. We present an intuitive approach where the optimal model of the likelihood-to-evidence ratio is found by maximizing the likelihood of simulated data. Within this framework, the connection between the task of simulation-based inference and mutual information maximization is clear, and we show how several known methods of posterior estimation relate to alternative lower bounds to mutual information. These distinct objective functions aim at the same optimal energy form and therefore can be directly benchmarked. We compare their accuracy in the inference of model parameters, focusing on four dynamical systems that encompass common challenges in time series analysis: dynamics driven by multiplicative noise, nonlinear interactions, chaotic behavior, and high-dimensional parameter space.  ( 2 min )

Various non-trivial spaces are becoming popular for embedding structured data such as graphs, texts, or images. Following spherical and hyperbolic spaces, more general product spaces have been proposed. However, searching for the best configuration of product space is a resource-intensive procedure, which reduces the practical applicability of the idea. We generalize the concept of product space and introduce an overlapping space that does not have the configuration search problem. The main idea is to allow subsets of coordinates to be shared between spaces of different types (Euclidean, hyperbolic, spherical). As a result, parameter optimization automatically learns the optimal configuration. Additionally, overlapping spaces allow for more compact representations since their geometry is more complex. Our experiments confirm that overlapping spaces outperform the competitors in graph embedding tasks. Here, we consider both distortion setup, where the aim is to preserve distances, and ranking setup, where the relative order should be preserved. The proposed method effectively solves the problem and outperforms the competitors in both settings. We also perform an empirical analysis in a realistic information retrieval task, where we compare all spaces by incorporating them into DSSM. In this case, the proposed overlapping space consistently achieves nearly optimal results without any configuration tuning. This allows for reducing training time, which can be significant in large-scale applications.  ( 2 min )

The noise transition matrix plays a central role in the problem of learning from noisy labels. Among many other reasons, a significant number of existing solutions rely on access to it. Estimating the transition matrix without using ground truth labels is a critical and challenging task. When label noise transition depends on each instance, the problem of identifying the instance-dependent noise transition matrix becomes substantially more challenging. Despite recent works proposing solutions for learning from instance-dependent noisy labels, we lack a unified understanding of when such a problem remains identifiable, and therefore learnable. This paper seeks to provide answers to a sequence of related questions: What are the primary factors that contribute to the identifiability of a noise transition matrix? Can we explain the observed empirical successes? When a problem is not identifiable, what can we do to make it so? We will relate our theoretical findings to the literature and hope to provide guidelines for developing effective solutions for battling instance-dependent label noise.  ( 2 min )

We study active sampling algorithms for linear regression, which aim to query only a few entries of a target vector $b\in\mathbb R^n$ and output a near minimizer to $\min_{x\in\mathbb R^d} \|Ax-b\|$, for a design matrix $A\in\mathbb R^{n \times d}$ and loss $\|\cdot\|$. For $p$ norm regression for any $0<p<\infty$, we give an algorithm based on Lewis weight sampling outputting a $(1+\epsilon)$-approximate solution using just $\tilde O(d/\epsilon^2)$ queries to $b$ for $p\in(0,1)$, $\tilde{O}(d/\epsilon)$ queries for $1<p<2$, and $\tilde{O}(d^{p/2}/\epsilon^p)$ queries for $2<p<\infty$. For $0<p<2$, our bounds are optimal up to log factors, settling the query complexity for this range. For $2<p<\infty$, our dependence on $d$ is optimal, while our dependence on $\epsilon$ is off by at most $\epsilon$, up to log factors. Our result resolves an open question of [CD21], who gave near optimal bounds for the $1$ norm, but required $d^2/\epsilon^2$ samples for $\ell_p$ regression with $1<p<2$, and gave no bounds for $2<p<\infty$ or $0<p<1$. We also give the first total sensitivity bound of $O(d^{\max\{1,p/2\}}\log^2n)$ for loss functions of degree $p$ polynomial growth, improving a result of [TMF20]. By combining this with our techniques for $\ell_p$ regression, we obtain an active regression algorithm making $\tilde O(d^{1+\max\{1,p/2\}}/\mathrm{poly}(\epsilon))$ queries for such loss functions, including the Tukey and Huber losses, answering another question of [CD21]. For the Huber loss, we further improve our bound to $\tilde O(d^{4-2\sqrt2}/\mathrm{poly}(\epsilon))$ samples. Our sensitivity bounds also have many applications, including Orlicz norm subspace embeddings, robust subspace approximation, and dimension reduction for smoothed $p$-norms. Finally, our active sampling results give the first sublinear time algorithms for Kronecker product regression under every $p$ norm.  ( 3 min )

We study the stochastic bilinear minimax optimization problem, presenting an analysis of the same-sample Stochastic ExtraGradient (SEG) method with constant step size, and presenting variations of the method that yield favorable convergence. In sharp contrasts with the basic SEG method whose last iterate only contracts to a fixed neighborhood of the Nash equilibrium, SEG augmented with iteration averaging provably converges to the Nash equilibrium under the same standard settings, and such a rate is further improved by incorporating a scheduled restarting procedure. In the interpolation setting where noise vanishes at the Nash equilibrium, we achieve an optimal convergence rate up to tight constants. We present numerical experiments that validate our theoretical findings and demonstrate the effectiveness of the SEG method when equipped with iteration averaging and restarting.  ( 2 min )

Sparse Bayesian learning (SBL) is a powerful framework for tackling the sparse coding problem while also providing uncertainty quantification. The most popular inference algorithms for SBL exhibit prohibitively large computational costs for high-dimensional problems due to the need to maintain a large covariance matrix. To resolve this issue, we introduce a new method for accelerating SBL inference -- named covariance-free expectation maximization (CoFEM) -- that avoids explicit computation of the covariance matrix. CoFEM solves multiple linear systems to obtain unbiased estimates of the posterior statistics needed by SBL. This is accomplished by exploiting innovations from numerical linear algebra such as preconditioned conjugate gradient and a little-known diagonal estimation rule. For a large class of compressed sensing matrices, we provide theoretical justifications for why our method scales well in high-dimensional settings. Through simulations, we show that CoFEM can be up to thousands of times faster than existing baselines without sacrificing coding accuracy. Through applications to calcium imaging deconvolution and multi-contrast MRI reconstruction, we show that CoFEM enables SBL to tractably tackle high-dimensional sparse coding problems of practical interest.  ( 2 min )

$Q$-learning is the most fundamental model-free reinforcement learning algorithm. Deployment of $Q$-learning requires approximation of the state-action value function (also known as the $Q$-function). In this work, we provide online random forests as $Q$-function approximators and propose a novel method wherein the random forest is grown as learning proceeds (through expanding forests). We demonstrate improved performance of our methods over state-of-the-art Deep $Q$-Networks in two OpenAI gyms (`blackjack' and `inverted pendulum') but not in the `lunar lander' gym. We suspect that the resilience to overfitting enjoyed by random forests recommends our method for common tasks that do not require a strong representation of the problem domain. We show that expanding forests (in which the number of trees increases as data comes in) improve performance, suggesting that expanding forests are viable for other applications of online random forests beyond the reinforcement learning setting.  ( 2 min )

Through solving pretext tasks, self-supervised learning leverages unlabeled data to extract useful latent representations replacing traditional input features in the downstream task. In audio/speech signal processing, a wide range of features where engineered through decades of research efforts. As it turns out, learning to predict such features (a.k.a pseudo-labels) has proven to be a particularly relevant pretext task, leading to useful self-supervised representations which prove to be effective for downstream tasks. However, methods and common practices for combining such pretext tasks for better performance on the downstream task have not been explored and understood properly. In fact, the process relies almost exclusively on a computationally heavy experimental procedure, which becomes intractable with the increase of the number of pretext tasks. This paper introduces a method to select a group of pretext tasks among a set of candidates. The method we propose estimates calibrated weights for the partial losses corresponding to the considered pretext tasks during the self-supervised training process. The experiments conducted on automatic speech recognition, speaker and emotion recognition validate our approach, as the groups selected and weighted with our method perform better than classic baselines, thus facilitating the selection and combination of relevant pseudo-labels for self-supervised representation learning.  ( 2 min )

We consider the problem of learning high dimensional polynomial transformations of Gaussians. Given samples of the form $p(x)$, where $x\sim N(0, \mathrm{Id}_r)$ is hidden and $p: \mathbb{R}^r \to \mathbb{R}^d$ is a function where every output coordinate is a low-degree polynomial, the goal is to learn the distribution over $p(x)$. This problem is natural in its own right, but is also an important special case of learning deep generative models, namely pushforwards of Gaussians under two-layer neural networks with polynomial activations. Understanding the learnability of such generative models is crucial to understanding why they perform so well in practice. Our first main result is a polynomial-time algorithm for learning quadratic transformations of Gaussians in a smoothed setting. Our second main result is a polynomial-time algorithm for learning constant-degree polynomial transformations of Gaussian in a smoothed setting, when the rank of the associated tensors is small. In fact our results extend to any rotation-invariant input distribution, not just Gaussian. These are the first end-to-end guarantees for learning a pushforward under a neural network with more than one layer. Along the way, we also give the first polynomial-time algorithms with provable guarantees for tensor ring decomposition, a popular generalization of tensor decomposition that is used in practice to implicitly store large tensors.  ( 2 min )

Graph neural networks (GNNs) have received massive attention in the field of machine learning on graphs. Inspired by the success of neural networks, a line of research has been conducted to train GNNs to deal with various tasks, such as node classification, graph classification, and link prediction. In this work, our task of interest is graph classification. Several GNN models have been proposed and shown great accuracy in this task. However, the question is whether usual training methods fully realize the capacity of the GNN models. In this work, we propose a two-stage training framework based on triplet loss. In the first stage, GNN is trained to map each graph to a Euclidean-space vector so that graphs of the same class are close while those of different classes are mapped far apart. Once graphs are well-separated based on labels, a classifier is trained to distinguish between different classes. This method is generic in the sense that it is compatible with any GNN model. By adapting five GNN models to our method, we demonstrate the consistent improvement in accuracy and utilization of each GNN's allocated capacity over the original training method of each model up to 5.4\% points in 12 datasets.  ( 2 min )

Deep learning has proven effective across a range of data sets. In light of this, a natural inquiry is: "for what data generating processes can deep learning succeed?" In this work, we study the sample complexity of learning multilayer data generating processes of a sort for which deep neural networks seem to be suited. We develop general and elegant information-theoretic tools that accommodate analysis of any data generating process -- shallow or deep, parametric or nonparametric, noiseless or noisy. We then use these tools to characterize the dependence of sample complexity on the depth of multilayer processes. Our results indicate roughly linear dependence on depth. This is in contrast to previous results that suggest exponential or high-order polynomial dependence.  ( 2 min )

The success and popularity of deep learning is on the rise, partially due to powerful deep learning frameworks such as TensorFlow and PyTorch that make it easier to develop deep learning models. However, these libraries also come with steep learning curves, since programming in these frameworks is quite different from traditional imperative programming with explicit loops and conditionals. In this work, we present a tool called TF-Coder for programming by example in TensorFlow. TF-Coder uses a bottom-up weighted enumerative search, with value-based pruning of equivalent expressions and flexible type- and value-based filtering to ensure that expressions adhere to various requirements imposed by the TensorFlow library. We train models to predict TensorFlow operations from features of the input and output tensors and natural language descriptions of tasks, to prioritize relevant operations during search. TF-Coder solves 63 of 70 real-world tasks within 5 minutes, sometimes finding simpler solutions in less time compared to experienced human programmers.  ( 2 min )

This paper is concerned with the training of neural networks (NNs) under semidefinite constraints, which allows for NN training with robustness and stability guarantees. In particular, we set up an efficient and scalable training scheme for NN training problems of this kind based on interior point methods, while we also exploit the structure of the underlying matrix constraint. We apply our training scheme to several relevant examples that have been studied in the literature and newly present the application of the method to the training of Wasserstein generative adversarial networks (WGANs). In numerical examples, we show the superiority of our method and its applicability to WGAN training.  ( 2 min )

We propose a new class of multiplier bootstraps for count functionals, ranging from a fast, approximate linear bootstrap tailored to sparse, massive graphs to a quadratic bootstrap procedure that offers refined accuracy for smaller, denser graphs. For the fast, approximate linear bootstrap, we show that $\sqrt{n}$-consistent inference of the count functional is attainable in certain computational regimes that depend on the sparsity level of the graph. Furthermore, even in more challenging regimes, we prove that our bootstrap procedure offers valid coverage and vanishing confidence intervals. For the quadratic bootstrap, we establish an Edgeworth expansion and show that this procedure offers higher-order accuracy under appropriate sparsity conditions. We complement our theoretical results with a simulation study and real data analysis and verify that our procedure offers state-of-the-art performance for several functionals.  ( 2 min )

The evaluation of the free energy of a stochastic model is considered to be a significant issue in various fields of physics and machine learning. However, the exact free energy evaluation is computationally infeasible because it includes an intractable partition function. Annealed importance sampling (AIS) is a type of importance sampling based on the Markov chain Monte Carlo method, which is similar to a simulated annealing, and can effectively approximate the free energy. This study proposes a new AIS-based approach, referred to as marginalized AIS (mAIS). The statistical efficiency of mAIS is investigated in detail based on a theoretical and numerical perspectives. Based on the investigation, it has been proved that mAIS is more effective than AIS under a certain condition.  ( 2 min )

Background: Selecting feature genes to predict phenotypes is one of the typical tasks in analyzing genomics data. Though many general-purpose algorithms were developed for prediction, dealing with highly correlated genes in the prediction model is still not well addressed. High correlation among genes introduces technical problems, such as multi-collinearity issues, leading to unreliable prediction models. Furthermore, when a causal gene (whose variants have an actual biological effect on a phenotype) is highly correlated with other genes, most algorithms select the feature gene from the correlated group in a purely data-driven manner. Since the correlation structure among genes could change substantially when condition changes, the prediction model based on not correctly selected feature genes is unreliable. Therefore, we aim to keep the causal biological signal in the prediction process and build a more robust prediction model. Method: We propose a grouping algorithm, which treats highly correlated genes as a group and uses their common pattern to represent the group's biological signal in feature selection. Our novel grouping algorithm can be integrated into existing prediction algorithms to enhance their prediction performance. Our proposed grouping method has two advantages. First, using the gene group's common patterns makes the prediction more robust and reliable under condition change. Second, it reports whole correlated gene groups as discovered biomarkers for prediction tasks, allowing researchers to conduct follow-up studies to identify causal genes within the identified groups. Result: Using real benchmark scRNA-seq datasets with simulated cell phenotypes, we demonstrate our novel method significantly outperforms standard models in both (1) prediction of cell phenotypes and (2) feature gene selection.  ( 2 min )

In the graph matching problem we observe two graphs $G,H$ and the goal is to find an assignment (or matching) between their vertices such that some measure of edge agreement is maximized. We assume in this work that the observed pair $G,H$ has been drawn from the correlated Wigner model -- a popular model for correlated weighted graphs -- where the entries of the adjacency matrices of $G$ and $H$ are independent Gaussians and each edge of $G$ is correlated with one edge of $H$ (determined by the unknown matching) with the edge correlation described by a parameter $\sigma\in [0,1)$. In this paper, we analyse the performance of the projected power method (PPM) as a seeded graph matching algorithm where we are given an initial partially correct matching (called the seed) as side information. We prove that if the seed is close enough to the ground-truth matching, then with high probability, PPM iteratively improves the seed and recovers the ground-truth matching (either partially or exactly) in $\mathcal{O}(\log n)$ iterations. Our results prove that PPM works even in regimes of constant $\sigma$, thus extending the analysis in (Mao et al.,2021) for the sparse Erd\"os-Renyi model to the (dense) Wigner model. As a byproduct of our analysis, we see that the PPM framework generalizes some of the state-of-art algorithms for seeded graph matching. We support and complement our theoretical findings with numerical experiments on synthetic data.  ( 2 min )

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So that the wrinkles and shadows are removed, etc. submitted by /u/xXNOdrugsForMEXx [link] [comments]

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I explained the basics of how a slide rule works in the previous post. But how does a circular slide rule work? Apparently the prop Mr. Spock is holding is an E6B aircraft slide rule. It includes a circular slide rule and more functionality. Start with an ordinary straight slide rule, with each bar labeled […] Circular slide rule first appeared on John D. Cook.  ( 2 min )

Suppose you have two sticks. The length of one is log x, and the length of the other is log y. If you put the two sticks end to end, the combined length is log x + log y = log xy. That’s the basic idea behind a slide rule. The simplest slide rule consists […] Why a slide rule works first appeared on John D. Cook.  ( 2 min )

This is a place to share machine learning research papers, journals, and articles that you're reading this week. If it relates to what you're researching, by all means elaborate and give us your insight, otherwise it could just be an interesting paper you've read. Please try to provide some insight from your understanding and please don't post things which are present in wiki. Preferably you should link the arxiv page (not the PDF, you can easily access the PDF from the summary page but not the other way around) or any other pertinent links. Previous weeks : 1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90 91-100 101-110 111-120 121-130 131-140 Week 1 Week 11 Week 21 Week 31 Week 41 Week 51 Week 61 Week 71 Week 81 Week 91 Week 101 Week 111 Week 121 Week 131 Week 2 Week 1…  ( 1 min )

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Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead! Thread will stay alive until next one so keep posting after the date in the title. Thanks to everyone for answering questions in the previous thread! submitted by /u/AutoModerator [link] [comments]  ( 2 min )

I want to perform image similarity between images from frames of 2 different movie trailers. I am currently using SSIM and VGG 16 individually. But ssim does not capture color differences and VGG 16 isn't capturing structural integrity. I can use both together, but I wanted to know if there is any metric or algorithm which can capture both together with less discrepancies and can capture both together. Will appreciate any help. Thank you! submitted by /u/terminatorash2199 [link] [comments]  ( 1 min )

Recurrent neural networks for brain time series - Sano Centre for Computational Personalised Medicine submitted by /u/alecrimi [link] [comments]

Hi everyone, I'd like to share with you an exciting upcoming live series by Prof. Richard Xu of Hong Kong Baptist University. If you're interested, please click here to register! Description: "I have been planning to start a machine learning live series on topics that involve some intermediate mathematics, so I can help you to clarify some concepts. In order to fully grasp these concepts, you need to have sound knowledge of linear algebra, calculus, statistics and probability. However, if you just want to come and hear it for fun, please do so as well! The first topic is variational autoencoders with normalized flow, which I'll fully explain its beautiful mathematics over a period of a few sessions. You can find my notes on my GitHub site: https://github.com/roboticcam/machine-learning-notes/blob/master/files/vb_nf.pdf I will post the Zoom link to the registered participants. Please join us!" submitted by /u/ML_Live_Series [link] [comments]  ( 1 min )

Hi all, We have a model used in cardiac MRi imaging, it is used to select the best image in a series of images. It consists of images -> Resnet -> LSTM -> output. The heatmap we generate from the Resnet alone shows output like the image attached, instead of actual anatomy, there is only little squares. We think this is likely due to the residual in the Resnet because it is not present in a VGG, but does anyone else have a better explanation, and an idea of how to visualise a Resnet? Resnet Saliency Map submitted by /u/Radiology_AI [link] [comments]  ( 1 min )

Hi reddit, happy to share our new paper "Critical analysis of deconfounded pretraining to improve visio-linguistic models". In a nutshell, it's on the problem of out-of-distribution performance for visio-linguistic models, and it takes a closer look / surfaces some issues with an existing technique for improving OOD performance by doing automatic deconfounding (inspired by the causality framework of Structural Causal Models). ​ Paper: https://www.frontiersin.org/articles/10.3389/frai.2022.736791/full Code: https://github.com/Natithan/p1_causality Abstract: An important problem with many current visio-linguistic models is that they often depend on spurious correlations. A typical example of a spurious correlation between two variables is one that is due to a third variable causing …  ( 2 min )

We often talk about how the publish-or-perish paradigm leads to constant minor improvements on the same problems (Image classification, text generation, etc). What are some of the best papers that do the opposite? Rather than solving known problems in a marginally better way, they solve a new problem with known (or modified) methods. submitted by /u/SuspiciousWalrus99 [link] [comments]  ( 2 min )

So, a bit of background, I am doing an RnD project in the area of improving the layout analysis of scientific documents. The proposed method is to use an active learning loop on standard object detection models and target those classes/layouts which are performing poorly and train the model based on them. Now, we have some selection strategies based on submodular selection functions to target the pages we want. I also have set up code to extract embeddings which will help me do the selection. But I don't have prior experience in active learning, especially setting it up with detectron2, because it seems to register a dataset to train, and it is really difficult to change dynamically in the middle of training, which is my use case. So I need some advice on the following:- The document analysis datasets are huge, DocBank is 50GB of images alone. How can I effectively store the embeddings in memory when I will call my selection algorithms mentioned above? How to set up an active learning loop in detectron2 for object detection. Or are there any alternatives? Some resources/code would be better There is some literature evidence suggesting that simple CNN backbone embeddings represent an image better than FasterRCNN or MaskRCNN embeddings. Specifically, this paper seems to be working on spliced image retrieval and it claims the following. Any thoughts/prior experience on this? Finally, is there any evidence supporting improvement in accuracy/precision in object detection using active learning? Or are there some better training paradigms? Thank you for your patience. submitted by /u/ExoticAd6868 [link] [comments]  ( 1 min )

I want to know more about Market Basket Analysis's real-world use case and unique insights/business value derived from performing the Association rule mining. I heard about the Beer-Diaper case study but many other sources invalidated it as a spurious correlation. Can someone share any example of business insights from Market Basket analysis and any interesting patterns they were able to observe?? submitted by /u/invincible_moron [link] [comments]  ( 2 min )

The examples I have seen so far for DMD and SINDy use only 1 trajectory of the dynamical system for training. The input data is a 2D matrix, with the states/features being one dimension and time being the other dimension. But I want to use multiple trajectories of the same dynamical system for training, so the training data would be 3D (i.e., multiple 2D matrices). Are there examples where this has been done? Linear regression techniques (like pseudoinverse or LASSO) seem to be used to get the system matrix (in DMD) or the weights for the features (in SINDy). Can these methods be extended to 3D input data? submitted by /u/baigyaanik [link] [comments]  ( 1 min )

The success and growth of AI is undeniable. Yet there are still basic tasks performing poorly, despite or because of automation. In some cases, you can blame reliance on outdated AI. In other cases, it is a result of corporate policies or multiple AI systems that compete against each other. The AI systems in question… Read More »6 Business Applications that Badly Need Better AI The post 6 Business Applications that Badly Need Better AI appeared first on Data Science Central.  ( 7 min )

How exactly do you define Artificial Intelligence(AI)? This looks like a back to basics/ back to school question – but the answer is not that simple Recently I was trying to find a good academic definition of AI for a research paper. Surprisingly, its not easy. In this post, I present a good definition for… Read More »How exactly do you define Artificial Intelligence(AI)? The post How exactly do you define Artificial Intelligence(AI)? appeared first on Data Science Central.  ( 2 min )

There are signs that Meta’s plans for the metaverse is faltering, including plummeting stock prices and the company’s announcement that it may withdraw from the EU market. The troubles stem from a myriad of issues, the most significant of which are data collection privacy issues and a lack of investor and public confidence in the… Read More »Public wary of Meta’s Metaverse vision The post Public wary of Meta’s Metaverse vision appeared first on Data Science Central.  ( 4 min )

NLQ or Natural Language Query or Text-to-SQL or NL2SQL is an arm of computational linguistics, that helps users to fetch required data, visualizations, and insights, from sentences written in human language. As a business user, knowing data schema, table and column names, knowing metadata, having technical know-how of a BI tool or data querying skills… Read More »NLQ: Why You Might Not Need To Call A Data Analyst Anymore The post NLQ: Why You Might Not Need To Call A Data Analyst Anymore appeared first on Data Science Central.  ( 4 min )

Profession in finance and accounting is one of the top career choices for finance and accounting professionals. Employment of accountants and auditors is projected to grow 7 percent from the year 2020 to the year 2030, about as fast as the average for all occupations. About 135,000 openings for accountants and auditors are projected each year,… Read More »Advance in your finance and accounting careers with top technical skills The post Advance in your finance and accounting careers with top technical skills appeared first on Data Science Central.  ( 3 min )

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In the field of artificial intelligence, reinforcement learning is a type of machine-learning strategy that rewards desirable behaviors while penalizing those which aren’t. An agent can perceive its surroundings and act accordingly through trial and error in general with this form or presence – it’s kind of like getting feedback on what works for you. However, learning rules from scratch in contexts with complex exploration problems is a big challenge in RL. Because the agent does not receive any intermediate incentives, it cannot determine how close it is to complete the goal. As a result, exploring the space at random becomes necessary until the door opens. Given the length of the task and the level of precision required, this is highly unlikely. Exploring the state space randomly with preliminary information should be avoided while performing this activity. This prior knowledge aids the agent in determining which states of the environment are desirable and should be investigated further. Offline data collected by human demonstrations, programmed policies, or other RL agents could be used to train a policy and then initiate a new RL policy. This would include copying the pre-trained policy’s neural network to the new RL policy in the scenario where we utilize neural networks to describe the procedures. This process transforms the new RL policy into a pre-trained one. However, as seen below, naively initializing a new RL policy like this frequently fails, especially for value-based RL approaches. Continue reading the summary Paper: https://arxiv.org/pdf/2204.02372.pdf Project: https://jumpstart-rl.github.io/ https://reddit.com/link/u0n5hv/video/fnktgf0wqqs81/player submitted by /u/No_Coffee_4638 [link] [comments]  ( 2 min )

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Cracking my head trying to figure out the differences between classical dynamic programming and policy iteration. I understand that policy iteration in itself is a form of dynamic programming. But if we were to compare the traditional operations of dynamic programming with policy iteration, what would be the differences. Thank you Heaps submitted by /u/BalramVeeragoo [link] [comments]  ( 1 min )

To my understanding, the Q value is updated like this: Q[s,a] = Q[s,a] + lr * (reward + gamma * max(Q[s,a]t+1) — Q[s,a]) Where future state reward is considered since the best current reward doesn't grantee the optimal path. E.g.: Path A: Q[s,a]t = 1, Q[s,a]t+1 = 10 Total: 11 Path B: Q[s,a]t = 5, Q[s,a]t+1 = 1 Total: 6 Not sure if this is a good analogy but here it gives the result that A is the optimal path even though its immediate reward at (t) is less than B. Further Question: Is there additional benefits to considering future rewards further than Q[s,a]t+1 ? Example: Q[s,a] = Q[s,a] + lr * (reward + gamma* max(Q[s,a]t+2) - gamma * max(Q[s,a]t+1) — Q[s,a]) submitted by /u/DangerNoodle314 [link] [comments]  ( 1 min )

Hi guys. I am currently implementing REDQ by modifying a working implementation of SAC (basically adapted from Spinup) and so far my implementation doesn't work, I am trying to understand why. By looking at the authors' implementation I notice they use 1 pytorch optimizer per Q network, whereas I only use 1 for all parameters. So I wonder, is there any good reason for using several optimizers here? Thanks! submitted by /u/yannbouteiller [link] [comments]  ( 1 min )

I am fairly new to RL. I'm trying to train an agent (like in gym's Cartpole env) in an environment with noisy (Gaussian noise) observation. I have added Gaussian noise to angle only (not to cart position, cart velocity or ang_velocity). I was fooling around with PPO in stable_baselines but haven't had much luck. Any suggestions on what needs to be tweaked or any good algorithm for this task? Also, I tried changing the default forcing magnitude of 10 to other values like 30 but it didn't help much. Thanks submitted by /u/Black_Beard53 [link] [comments]  ( 2 min )

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I have been into the website of OpenAI, it is unclear what has been added to Dall-e2, why should we subscribe for a github which will be made public? And what is the color code at the bottom? submitted by /u/pasticciociccio [link] [comments]  ( 1 min )

When deep networks or inputs involve extensive data sequences, learning in neural networks can be unstable. Recurrent states in vanilla recurrent neural networks (RNNs) are generated by repeatedly applying a linear transformation followed by a pointwise nonlinearity. This becomes unstable when the linear transformation’s eigenvalues are not of magnitude one. Unitary matrices have been utilized to solve the problem of disappearing and exploding gradients because they have eigenvalues of size one, naturally, and have been. Unitary convolutional layers have recently been developed in a similar way to aid in the development of more stable deep networks with norm-preserving transformations. The loss function’s derivative with respect to the weights is called a gradient. During backpropagation in neural networks, it is utilized to update the weights to minimize the loss function. When traveled backward with each layer, the derivative or slope continuously grows lower, resulting in a vanishing gradient. When the weight update is exponentially small, the training time is excessively long. In the worst-case scenario, the neural network training may be stopped entirely. Exploding gradients, on the other hand, occur when the slope increases with each successive layer during backpropagation. The gradient will never converge due to the high weights, causing it to oscillate around the minima without ever reaching a global minima point. Continue Reading Paper: https://arxiv.org/pdf/2203.05483.pdf submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

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https://preview.redd.it/pkrbloq8vjs81.png?width=1422&format=png&auto=webp&s=fef693165a6c948f626de613e4e341c25f8cf5f4 ​ Extreme-scale language models have recently exhibited incredible performance on natural language processing challenges. This is due to their ever-increasing size, exceeding 500 billion parameters. However, while these models have grown in popularity in recent years, the amount of data utilized to train them has not increased. The current generation of huge language models is clearly undertrained. Three prediction approaches for optimally choosing both model size and training length have been proposed by a DeepMind research team. Three approaches have been mentioned to estimate the optimal parameter: Change the size of the models and the number of training tokens. IsoFLOP profiles Using a parametric loss function to fit a model The ultimate pretraining loss is calculated as the number of model parameters and training tokens. They minimize the loss function under the restriction of the FLOPs function, which is equal to the computational budget because the computational budget is a probabilistic function of the number of observed training tokens and model parameters. Continue Reading This Research Summary Paper: https://arxiv.org/pdf/2203.15556.pdf submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

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I'm about to graduate with a master's degree in Computer Science and I'm very passionate about Affective Computing. I would like to start looking for a job in this field, but most companies (not consulting) are looking for people with experience or a PhD. What do you recommend me to do? Continue with the PhD or try to find something, maybe in some startup? submitted by /u/_rikya_ [link] [comments]  ( 1 min )

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I'm joining MSc AI & ML this September. I want to buy a laptop. Is MacBook Air sufficient for this? If not what would you recommend to someone like me? submitted by /u/RauhanSheikh [link] [comments]  ( 1 min )

Please give a thorough and in-depth response. submitted by /u/trillswan [link] [comments]  ( 1 min )

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I'm looking into implementing a replay buffer in A2C. I came upon the ACER [paper](https://arxiv.org/pdf/1611.01224.pdf). From my understanding, it looks like ACER is an extension of A3C, and it seems like the difference between A2C and A3C is that in A2C parameters are updated synchronously and that helps with big batch sizes. Is it still possible to implement some kind of replay buffer on A2C? Are there any papers that involve implementing a paper with A2C that you recommend I read? I'm new to the area of reinforcement learning, so I would be very grateful for any kind of help you can offer. Thanks in advance submitted by /u/lebr0n99 [link] [comments]  ( 1 min )

Supposedly there is a popular discord server for the RL community, however I am having difficulty finding it. submitted by /u/jclaessens [link] [comments]

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Hello friends, I'm looking for some resources that would let me quickly start with Reinforcement Learning (preferably in Python). I have some experience with supervised learning (e.g. deep nets) and would like to complement with some RL. Preferably a walkthrough with some examples of implementation. Can you recommend something? Thanks in advance! submitted by /u/andy-codes [link] [comments]  ( 1 min )

So learning about e-mail learning same have it all understood except for the max thingy. If you care enough to click this blog it's not my blog): https://towardsdatascience.com/simple-reinforcement-learning-q-learning-fcddc4b6fe56 I don't know how to turn this into to a real example: Update q values Q[state, action] = Q[state, action] + lr * (reward + gamma * np.max(Q[new_state, :]) — Q[state, action]) Specifically the last bit: np.max(Q[new_state, :]) — Q[state, action]) What does the numpy max actually operate on here? Any hard examples? Thanks. submitted by /u/Togfox [link] [comments]  ( 2 min )

#cvpr-2022 Happy to share our CVPR-2022 paper "Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning" Paper: https://arxiv.org/pdf/2111.14213.pdf Code: https://github.com/mmendiet/FedAlign Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices). However, the data distribution among clients is often non-IID in nature, making efficient optimization difficult. To alleviate this issue, many FL algorithms focus on mitigating the effects of data heterogeneity across clients by introducing a variety of proximal terms, some incurring considerable compute and/or memory overheads, to restrain local updates with respect to the global model. Instead, we consider rethinking solutions to data heterogeneity in FL with a focus on local learning generality rather than proximal restriction. To this end, we first present a systematic study informed by second-order indicators to better understand algorithm effectiveness in FL. Interestingly, we find that standard regularization methods are surprisingly strong performers in mitigating data heterogeneity effects. Based on our findings, we further propose a simple and effective method, FedAlign, to overcome data heterogeneity and the pitfalls of previous methods. FedAlign achieves competitive accuracy with state-of-the-art FL methods across a variety of settings while minimizing computation and memory overhead. submitted by /u/Extension-Sun1816 [link] [comments]  ( 1 min )

I was wondering if the Domain conflicts system working well? I got an email from the PCs and seems that the domain conflict system is not working well. It said that we can enter the conflict now but I cannot edit the conflict in the system. Could anyone tell me how to do it? Thanks! Dear ICML Authors, As we are seeing this happen, we just wanted to send you a brief explanation -- this only applies to some few papers. A few papers are losing reviews because of newly arising conflicts. If you did not enter your conflicts in CMT during the submission phase (as requested via CMT), then they could not be used in paper assignments. If you enter them now, any reviews by reviewers with conflicting domains will disappear, and you may see fewer reviews as a result. Unfortunately, we have no control over this, as the conflicts should have been entered when the paper was submitted. Best, Stefanie, Le and Csaba submitted by /u/Snoo_97274 [link] [comments]  ( 1 min )

View Poll submitted by /u/martolini [link] [comments]  ( 1 min )

submitted by /u/Illustrious_Row_9971 [link] [comments]  ( 1 min )

Hi everyone, I built a small library to help with hyperparameter search for deep learning models created with pytorch, because I got kinda tired of having to rewrite large pieces code over and over again. You can check it out here: https://github.com/michi-jeremias/tinydl or you can even install it with pip (pip install tinydl). I have included a readme and an example of how the library can be used. About the library, it's pretty flexible about reporting different metrics to the console and to tensorboard (add_scalar, add_hparam) at each stage of the process, like after a batch, epoch of after a whole run over multiple epochs. It can also be easily extended to include other metrics or new types of outputs. Since this is basically my first attempt at a software project that's not intended only to be used by myself I'd be happy about any feedback you have for me! If the project doesn't qualify to be posted here due to being too simple/too much on a beginner level, apologies for that. submitted by /u/abacaxiquaxi [link] [comments]  ( 1 min )

I am trying to implement stylegan2 and there are so many things here that are not explained either well, or at all in the paper. ​ How exactly is path length regularization implemented? In this PT code we can see that the $|J^T_w.y|$ is computed as follows: ​ def g_path_regularize(fake_img, latents, mean_path_length, decay=0.01): noise = torch.randn_like(fake_img) / math.sqrt( fake_img.shape[2] * fake_img.shape[3] ) grad, = autograd.grad( outputs=(fake_img * noise).sum(), inputs=latents, create_graph=True ) path_lengths = torch.sqrt(grad.pow(2).sum(2).mean(1)) path_mean = mean_path_length + decay * (path_lengths.mean() - mean_path_length) path_penalty = (path_lengths - path_mean).pow(2).mean() return path_penalty, path_mean.detach(), path_lengths This is based on this official TF implementation. The problem I have is that from what I understand, fake_img is 4D, and latents is 2D. The grad output in this case will be 2D and grad.pow(2).sum(2) cannot be computed because the third axis does not exist. Obviously people who are using these repos have not reported any issue regarding mismatch of shapes and axes, so I believe there is something else going on. Since I'm trying to implement this in my own network, I cannot get the desired shape any how. I get a 2D gradient output. submitted by /u/feryet [link] [comments]  ( 1 min )

I released a repository of models with optional pretrained weights(Weights are taken from TF/Keras) to be used for tasks like prediction, feature extraction and fine-tuning. Github: https://github.com/abarcel/haikumodels Currently Available Models MobileNet ResNet [50, 101, 152] VGG [16, 19] Xception Also planning to release more, as soon as I find time for it. submitted by /u/abarcel [link] [comments]

I spent some time reading about and playing around with speech denoising DNNs ~2019. At the time the popular architecture was U-Net (encoder -> bottleneck -> decoder with skip connections) operating on spectrograms. These U-Nets were trained directly on noisy/clean speech pairs and the loss was the difference between the predicted denoised images and actual denoised image. MSE between the predicted/actual images was a baseline loss but people alse added "feature loss" or sometimes a GAN-based loss function as well. Anyway a cursory reading of the DALL-E 2 paper has me thinking about that approach. I'm curious to know if a similar approach used for DALL-E has been tried for audio denoising: pre-train an encoder/decoder in a self-supervised fashion on a large dataset of audio train a denoiser to operate only in the latent space (ie the most compressed representation that is passed from the encoder to the decoder) step 1 - self-supervised training of encoder/decoder https://preview.redd.it/nprc40ob9gs81.png?width=1668&format=png&auto=webp&s=3a9b181ceff6c4530b5f41abf793dfb6409c0ec2 step 2 - train denoiser in latent space only ​ https://preview.redd.it/hreajdpe9gs81.png?width=1279&format=png&auto=webp&s=e16490fff00fe82487ca214b11b642ffcb30fb1c step 3 - do inference by feeding denoised latent space vector into the decoder https://preview.redd.it/7nox4ezh9gs81.png?width=2034&format=png&auto=webp&s=ccc69bbb2096d84a9e4a824000e62bb0f80fbe29 Is this a common approach already? It seems like once you have a good pretrained encoder/decoder pair then the denoiser training would be much more efficient than training an entire network that does everything at once from scratch (smaller search space, faster training loop) submitted by /u/The_Amp_Walrus [link] [comments]  ( 1 min )

Hey guys, I have the opportunity to either move to the platform engineering team or the freshly created MLOps team within my company. I'm interested in both careers, as I like to build Infra. I'm currently a Data Eng, and I find myself to like building apps and enabling applications to talk to each other, better than cleaning up data. I worked as a data scientist before, but I didn't like the science. I was always into engineering. What would make sense from a career perspective (both long and short term), i.e., money, promotions, attractiveness, etc. submitted by /u/dash2392 [link] [comments]  ( 1 min )

Purpose of review: Recent advances in sensing, actuation, and computation have opened the door to multi-robot systems consisting of hundreds/thousands of robots, with promising applications to automated manufacturing, disaster relief, harvesting, last-mile delivery, port/airport operations, or search and rescue. The community has leveraged model-free multi-agent reinforcement learning (MARL) to devise efficient, scalable controllers for multi-robot systems (MRS). This review aims to provide an analysis of the state-of-the-art in distributed MARL for multi-robot cooperation. Recent findings: Decentralized MRS face fundamental challenges, such as non-stationarity and partial observability. Building upon the "centralized training, decentralized execution" paradigm, recent MARL approaches include independent learning, centralized critic, value decomposition, and communication learning approaches. Cooperative behaviors are demonstrated through AI benchmarks and fundamental real-world robotic capabilities such as multi-robot motion/path planning. Summary: This survey reports the challenges surrounding decentralized model-free MARL for multi-robot cooperation and existing classes of approaches. We present benchmarks and robotic applications along with a discussion on current open avenues for research.  ( 2 min )

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure -- this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis.  ( 2 min )

Streamlined qBOLD acquisitions enable experimentally straightforward observations of brain oxygen metabolism. $R_2^\prime$ maps are easily inferred; however, the Oxygen extraction fraction (OEF) and deoxygenated blood volume (DBV) are more ambiguously determined from the data. As such, existing inference methods tend to yield very noisy and underestimated OEF maps, while overestimating DBV. This work describes a novel probabilistic machine learning approach that can infer plausible distributions of OEF and DBV. Initially, we create a model that produces informative voxelwise prior distribution based on synthetic training data. Contrary to prior work, we model the joint distribution of OEF and DBV through a scaled multivariate logit-Normal distribution, which enables the values to be constrained within a plausible range. The prior distribution model is used to train an efficient amortized variational Bayesian inference model. This model learns to infer OEF and DBV by predicting real image data, with few training data required, using the signal equations as a forward model. We demonstrate that our approach enables the inference of smooth OEF and DBV maps, with a physiologically plausible distribution that can be adapted through specification of an informative prior distribution. Other benefits include model comparison (via the evidence lower bound) and uncertainty quantification for identifying image artefacts. Results are demonstrated on a small study comparing subjects undergoing hyperventilation and at rest. We illustrate that the proposed approach allows measurement of gray matter differences in OEF and DBV and enables voxelwise comparison between conditions, where we observe significant increases in OEF and $R_2^\prime$ during hyperventilation.  ( 2 min )

We consider the problem of computing an equilibrium in a class of nonlinear generalized Nash equilibrium problems (NGNEPs) in which the strategy sets for each player are defined by equality and inequality constraints that may depend on the choices of rival players. While the asymptotic global convergence and local convergence rate of solution procedures have been studied in this setting, the analysis of iteration complexity is still in its infancy. Our contribution is to provide two simple first-order algorithmic frameworks based on the quadratic penalty method and the augmented Lagrangian method, respectively, with an accelerated mirror-prox algorithm as the inner loop. We provide nonasymptotic theoretical guarantees for these algorithms. More specifically, we establish the global convergence rate of our algorithms for solving (strongly) monotone NGNEPs and we provide iteration complexity bounds expressed in terms of the number of gradient evaluations. Experimental results demonstrate the efficiency of our algorithms.  ( 2 min )

Mean opinion score (MOS) is a typical subjective evaluation metric for speech synthesis systems. Since collecting MOS is time-consuming, it would be desirable if there are accurate MOS prediction models for automatic evaluation. In this work, we propose DDOS, a novel MOS prediction model. DDOS utilizes domain adaptive pre-training to further pre-train self-supervised learning models on synthetic speech. And a proposed module is added to model the opinion score distribution of each utterance. With the proposed components, DDOS outperforms previous works on BVCC dataset. And the zero shot transfer result on BC2019 dataset is significantly improved. DDOS also wins second place in Interspeech 2022 VoiceMOS challenge in terms of system-level score.  ( 2 min )

Unsupervised image-to-image translation aims to learn the translation between two visual domains without paired data. Despite the recent progress in image translation models, it remains challenging to build mappings between complex domains with drastic visual discrepancies. In this work, we present a novel framework, Generative Prior-guided UNsupervised Image-to-image Translation (GP-UNIT), to improve the overall quality and applicability of the translation algorithm. Our key insight is to leverage the generative prior from pre-trained class-conditional GANs (e.g., BigGAN) to learn rich content correspondences across various domains. We propose a novel coarse-to-fine scheme: we first distill the generative prior to capture a robust coarse-level content representation that can link objects at an abstract semantic level, based on which fine-level content features are adaptively learned for more accurate multi-level content correspondences. Extensive experiments demonstrate the superiority of our versatile framework over state-of-the-art methods in robust, high-quality and diversified translations, even for challenging and distant domains.  ( 2 min )

In this work, we present the SOMOS dataset, the first large-scale mean opinion scores (MOS) dataset consisting of solely neural text-to-speech (TTS) samples. It can be employed to train automatic MOS prediction systems focused on the assessment of modern synthesizers, and can stimulate advancements in acoustic model evaluation. It consists of 20K synthetic utterances of the LJ Speech voice, a public domain speech dataset which is a common benchmark for building neural acoustic models and vocoders. Utterances are generated from 200 TTS systems including vanilla neural acoustic models as well as models which allow prosodic variations. An LPCNet vocoder is used for all systems, so that the samples' variation depends only on the acoustic models. The synthesized utterances provide balanced and adequate domain and length coverage. We collect MOS naturalness evaluations on 3 English Amazon Mechanical Turk locales and share practices leading to reliable crowdsourced annotations for this task. Baseline results of state-of-the-art MOS prediction models on the SOMOS dataset are presented, while we show the challenges that such models face when assigned to evaluate synthetic utterances.  ( 2 min )

Time series data occurs widely, and outlier detection is a fundamental problem in data mining, which has numerous applications. Existing autoencoder-based approaches deliver state-of-the-art performance on challenging real-world data but are vulnerable to outliers and exhibit low explainability. To address these two limitations, we propose robust and explainable unsupervised autoencoder frameworks that decompose an input time series into a clean time series and an outlier time series using autoencoders. Improved explainability is achieved because clean time series are better explained with easy-to-understand patterns such as trends and periodicities. We provide insight into this by means of a post-hoc explainability analysis and empirical studies. In addition, since outliers are separated from clean time series iteratively, our approach offers improved robustness to outliers, which in turn improves accuracy. We evaluate our approach on five real-world datasets and report improvements over the state-of-the-art approaches in terms of robustness and explainability. This is an extended version of "Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection", to appear in IEEE ICDE 2022.  ( 2 min )

Rapid advancements in deep learning have led to many recent breakthroughs. While deep learning models achieve superior performance, often statistically better than humans, their adoption into safety-critical settings, such as healthcare or self-driving cars is hindered by their inability to provide safety guarantees or to expose the inner workings of the model in a human understandable form. We present Mo\"ET, a novel model based on Mixture of Experts, consisting of decision tree experts and a generalized linear model gating function. Thanks to such gating function the model is more expressive than the standard decision tree. To support non-differentiable decision trees as experts, we formulate a novel training procedure. In addition, we introduce a hard thresholding version, Mo\"ETH, in which predictions are made solely by a single expert chosen via the gating function. Thanks to that property, Mo\"ETH allows each prediction to be easily decomposed into a set of logical rules in a form which can be easily verified. While Mo\"ET is a general use model, we illustrate its power in the reinforcement learning setting. By training Mo\"ET models using an imitation learning procedure on deep RL agents we outperform the previous state-of-the-art technique based on decision trees while preserving the verifiability of the models. Moreover, we show that Mo\"ET can also be used in real-world supervised problems on which it outperforms other verifiable machine learning models.  ( 3 min )

The Shapley value (SV) and Least core (LC) are classic methods in cooperative game theory for cost/profit sharing problems. Both methods have recently been proposed as a principled solution for data valuation tasks, i.e., quantifying the contribution of individual datum in machine learning. However, both SV and LC suffer computational challenges due to the need for retraining models on combinatorially many data subsets. In this work, we propose to boost the efficiency in computing Shapley value or Least core by learning to estimate the performance of a learning algorithm on unseen data combinations. Theoretically, we derive bounds relating the error in the predicted learning performance to the approximation error in SV and LC. Empirically, we show that the proposed method can significantly improve the accuracy of SV and LC estimation.  ( 2 min )

Supervised federated learning (FL) enables multiple clients to share the trained model without sharing their labeled data. However, potential clients might even be reluctant to label their own data, which could limit the applicability of FL in practice. In this paper, we show the possibility of unsupervised FL whose model is still a classifier for predicting class labels, if the class-prior probabilities are shifted while the class-conditional distributions are shared among the unlabeled data owned by the clients. We propose federation of unsupervised learning (FedUL), where the unlabeled data are transformed into surrogate labeled data for each of the clients, a modified model is trained by supervised FL, and the wanted model is recovered from the modified model. FedUL is a very general solution to unsupervised FL: it is compatible with many supervised FL methods, and the recovery of the wanted model can be theoretically guaranteed as if the data have been labeled. Experiments on benchmark and real-world datasets demonstrate the effectiveness of FedUL. Code is available at https://github.com/lunanbit/FedUL.  ( 2 min )

Despite the success of Graph Neural Networks (GNNs) on various applications, GNNs encounter significant performance degradation when the amount of supervision signals, i.e., number of labeled nodes, is limited, which is expected as GNNs are trained solely based on the supervision obtained from the labeled nodes. On the other hand,recent self-supervised learning paradigm aims to train GNNs by solving pretext tasks that do not require any labeled nodes, and it has shown to even outperform GNNs trained with few labeled nodes. However, a major drawback of self-supervised methods is that they fall short of learning class discriminative node representations since no labeled information is utilized during training. To this end, we propose a novel semi-supervised method for graphs, GraFN, that leverages few labeled nodes to ensure nodes that belong to the same class to be grouped together, thereby achieving the best of both worlds of semi-supervised and self-supervised methods. Specifically, GraFN randomly samples support nodes from labeled nodes and anchor nodes from the entire graph. Then, it minimizes the difference between two predicted class distributions that are non-parametrically assigned by anchor-supports similarity from two differently augmented graphs. We experimentally show that GraFN surpasses both the semi-supervised and self-supervised methods in terms of node classification on real-world graphs. The source code for GraFN is available at https://github.com/Junseok0207/GraFN.  ( 2 min )

Adopting reinforcement learning (RL) for traffic signal control is increasingly popular. Most RL methods use fixed action interval (denoted as tduration) and actuate or maintain a phase every tduration, which makes the phase duration less dynamic and flexible. In addition, the actuated phase can be arbitrary, affecting the real-world deployment, which requires a fixed cyclical phase structure. To address these challenges, we propose a multi-level traffic signal control framework, DynLight, which uses an optimization method Max-QueueLength (M-QL) to determine the phase and uses a deep Q-network to determine the corresponding duration. Based on DynLight, we further propose DynLight-C that adopts a well trained deep Q-network of DynLight and replace M-QL by a fixed cyclical control policy that actuate a set of phases in fixed order to realize cyclical phase structure. Comprehensive experiments on multiple real-world datasets demonstrate that DynLight achives a new state-of-the-art. Furthermore, the deep Q-network of DynLight can learn well on determining the phase duration and DynLight-C demonstrates high performance for deployment.  ( 2 min )

Learning problems commonly exhibit an interesting feedback mechanism wherein the population data reacts to competing decision makers' actions. This paper formulates a new game theoretic framework for this phenomenon, called "multi-player performative prediction". We focus on two distinct solution concepts, namely (i) performatively stable equilibria and (ii) Nash equilibria of the game. The latter equilibria are arguably more informative, but can be found efficiently only when the game is monotone. We show that under mild assumptions, the performatively stable equilibria can be found efficiently by a variety of algorithms, including repeated retraining and the repeated (stochastic) gradient method. We then establish transparent sufficient conditions for strong monotonicity of the game and use them to develop algorithms for finding Nash equilibria. We investigate derivative free methods and adaptive gradient algorithms wherein each player alternates between learning a parametric description of their distribution and gradient steps on the empirical risk. Synthetic and semi-synthetic numerical experiments illustrate the results.

Although evaluation of the expectations on the Ising model is essential in various applications, this is frequently infeasible because of intractable multiple summations (or integrations). Spatial Monte Carlo integration (SMCI) is a sampling-based approximation, and can provide high-accuracy estimations for such intractable expectations. To evaluate the expectation of a function of variables in a specific region (called target region), SMCI considers a larger region containing the target region (called sum region). In SMCI, the multiple summation for the variables in the sum region is precisely executed, and that in the outer region is evaluated by the sampling approximation such as the standard Monte Carlo integration. It is guaranteed that the accuracy of the SMCI estimator is monotonically improved as the size of the sum region increases. However, a haphazard expansion of the sum region could cause a combinatorial explosion. Therefore, we hope to improve the accuracy without such region expansion. In this study, based on the theory of generalized least squares, a new effective method is proposed by combining multiple SMCI estimators. The validity of the proposed method is demonstrated theoretically and numerically. The results indicate that the proposed method can be effective in the inverse Ising problem (or Boltzmann machine learning).  ( 2 min )

While exam-style questions are a fundamental educational tool serving a variety of purposes, manual construction of questions is a complex process that requires training, experience and resources. Automatic question generation (QG) techniques can be utilized to satisfy the need for a continuous supply of new questions by streamlining their generation. However, compared to automatic question answering (QA), QG is a more challenging task. In this work, we fine-tune a multilingual T5 (mT5) transformer in a multi-task setting for QA, QG and answer extraction tasks using Turkish QA datasets. To the best of our knowledge, this is the first academic work that performs automated text-to-text question generation from Turkish texts. Experimental evaluations show that the proposed multi-task setting achieves state-of-the-art Turkish question answering and question generation performance on TQuADv1, TQuADv2 datasets and XQuAD Turkish split. The source code and the pre-trained models are available at https://github.com/obss/turkish-question-generation.

A new data-driven method for operator learning of stochastic differential equations(SDE) is proposed in this paper. The central goal is to solve forward and inverse stochastic problems more effectively using limited data. Deep operator network(DeepONet) has been proposed recently for operator learning. Compared to other neural networks to learn functions, it aims at the problem of learning nonlinear operators. However, it can be challenging by using the original model to learn nonlinear operators for high-dimensional stochastic problems. We propose a new multi-resolution autoencoder DeepONet model referred to as MultiAuto-DeepONet to deal with this difficulty with the aid of convolutional autoencoder. The encoder part of the network is designed to reduce the dimensionality as well as discover the hidden features of high-dimensional stochastic inputs. The decoder is designed to have a special structure, i.e. in the form of DeepONet. The first DeepONet in decoder is designed to reconstruct the input function involving randomness while the second one is used to approximate the solution of desired equations. Those two DeepONets has a common branch net and two independent trunk nets. This architecture enables us to deal with multi-resolution inputs naturally. By adding $L_1$ regularization to our network, we found the outputs from the branch net and two trunk nets all have sparse structures. This reduces the number of trainable parameters in the neural network thus making the model more efficient. Finally, we conduct several numerical experiments to illustrate the effectiveness of our proposed MultiAuto-DeepONet model with uncertainty quantification.  ( 2 min )

This work presents the first study of using the popular Monte Carlo Tree Search (MCTS) method combined with dedicated heuristics for solving the Weighted Vertex Coloring Problem. Starting with the basic MCTS algorithm, we gradually introduce a number of algorithmic variants where MCTS is extended by various simulation strategies including greedy and local search heuristics. We conduct experiments on well-known benchmark instances to assess the value of each studied combination. We also provide empirical evidence to shed light on the advantages and limits of each strategy.

Artificial neural networks (ANN), typically referred to as neural networks, are a class of Machine Learning algorithms and have achieved widespread success, having been inspired by the biological structure of the human brain. Neural networks are inherently powerful due to their ability to learn complex function approximations from data. This generalization ability has been able to impact multidisciplinary areas involving image recognition, speech recognition, natural language processing, and others. Activation functions are a crucial sub-component of neural networks. They define the output of a node in the network given a set of inputs. This survey discusses the main concepts of activation functions in neural networks, including; a brief introduction to deep neural networks, a summary of what are activation functions and how they are used in neural networks, their most common properties, the different types of activation functions, some of the challenges, limitations, and alternative solutions faced by activation functions, concluding with the final remarks.

Principal component analysis (PCA) is a dimensionality reduction method in data analysis that involves diagonalizing the covariance matrix of the dataset. Recently, quantum algorithms have been formulated for PCA based on diagonalizing a density matrix. These algorithms assume that the covariance matrix can be encoded in a density matrix, but a concrete protocol for this encoding has been lacking. Our work aims to address this gap. Assuming amplitude encoding of the data, with the data given by the ensemble $\{p_i,| \psi_i \rangle\}$, then one can easily prepare the ensemble average density matrix $\overline{\rho} = \sum_i p_i |\psi_i\rangle \langle \psi_i |$. We first show that $\overline{\rho}$ is precisely the covariance matrix whenever the dataset is centered. For quantum datasets, we exploit global phase symmetry to argue that there always exists a centered dataset consistent with $\overline{\rho}$, and hence $\overline{\rho}$ can always be interpreted as a covariance matrix. This provides a simple means for preparing the covariance matrix for arbitrary quantum datasets or centered classical datasets. For uncentered classical datasets, our method is so-called "PCA without centering", which we interpret as PCA on a symmetrized dataset. We argue that this closely corresponds to standard PCA, and we derive equations and inequalities that bound the deviation of the spectrum obtained with our method from that of standard PCA. We numerically illustrate our method for the MNIST handwritten digit dataset. We also argue that PCA on quantum datasets is natural and meaningful, and we numerically implement our method for molecular ground-state datasets.

Developers frequently use APIs to implement certain functionalities, such as parsing Excel Files, reading and writing text files line by line, etc. Developers can greatly benefit from automatic API usage sequence generation based on natural language queries for building applications in a faster and cleaner manner. Existing approaches utilize information retrieval models to search for matching API sequences given a query or use RNN-based encoder-decoder to generate API sequences. As it stands, the first approach treats queries and API names as bags of words. It lacks deep comprehension of the semantics of the queries. The latter approach adapts a neural language model to encode a user query into a fixed-length context vector and generate API sequences from the context vector. We want to understand the effectiveness of recent Pre-trained Transformer based Models (PTMs) for the API learning task. These PTMs are trained on large natural language corpora in an unsupervised manner to retain contextual knowledge about the language and have found success in solving similar Natural Language Processing (NLP) problems. However, the applicability of PTMs has not yet been explored for the API sequence generation task. We use a dataset that contains 7 million annotations collected from GitHub to evaluate the PTMs empirically. This dataset was also used to assess previous approaches. Based on our results, PTMs generate more accurate API sequences and outperform other related methods by around 11%. We have also identified two different tokenization approaches that can contribute to a significant boost in PTMs' performance for the API sequence generation task.

This paper presents a new Android malware detection method based on Graph Neural Networks (GNNs) with Jumping-Knowledge (JK). Android function call graphs (FCGs) consist of a set of program functions and their inter-procedural calls. Thus, this paper proposes a GNN-based method for Android malware detection by capturing meaningful intra-procedural call path patterns. In addition, a Jumping-Knowledge technique is applied to minimize the effect of the over-smoothing problem, which is common in GNNs. The proposed method has been extensively evaluated using two benchmark datasets. The results demonstrate the superiority of our approach compared to state-of-the-art approaches in terms of key classification metrics, which demonstrates the potential of GNNs in Android malware detection and classification.

In this paper, we consider the federated learning (FL) problem in the presence of communication errors. We model the link between the devices and the central node (CN) by a packet erasure channel, where the local parameters from devices are either erased or received correctly by CN with probability $e$ and $1-e$, respectively. We provide mathematical proof for the convergence of the FL algorithm in the presence of communication errors, where the CN uses past local updates when the fresh updates are not received from some devices. We show via simulations that by using the past local updates, the FL algorithm can converge in the presence of communication errors. We also show that when the dataset is uniformly distributed among devices, the FL algorithm that only uses fresh updates and discards missing updates might converge faster than the FL algorithm that uses past local updates.

Some of the most severe bottlenecks preventing widespread development of machine learning models for human behavior include a dearth of labeled training data and difficulty of acquiring high quality labels. Active learning is a paradigm for using algorithms to computationally select a useful subset of data points to label using metrics for model uncertainty and data similarity. We explore active learning for naturalistic computer vision emotion data, a particularly heterogeneous and complex data space due to inherently subjective labels. Using frames collected from gameplay acquired from a therapeutic smartphone game for children with autism, we run a simulation of active learning using gameplay prompts as metadata to aid in the active learning process. We find that active learning using information generated during gameplay slightly outperforms random selection of the same number of labeled frames. We next investigate a method to conduct active learning with subjective data, such as in affective computing, and where multiple crowdsourced labels can be acquired for each image. Using the Child Affective Facial Expression (CAFE) dataset, we simulate an active learning process for crowdsourcing many labels and find that prioritizing frames using the entropy of the crowdsourced label distribution results in lower categorical cross-entropy loss compared to random frame selection. Collectively, these results demonstrate pilot evaluations of two novel active learning approaches for subjective affective data collected in noisy settings.

Commit messages concisely describe the content of code diffs (i.e., code changes) and the intent behind them. Recently, many approaches have been proposed to generate commit messages automatically. The information retrieval-based methods reuse the commit messages of similar code diffs, while the neural-based methods learn the semantic connection between code diffs and commit messages. However, the reused commit messages might not accurately describe the content/intent of code diffs and neural-based methods tend to generate high-frequent and repetitive tokens in the corpus. In this paper, we combine the advantages of the two technical routes and propose a novel exemplar-based neural commit message generation model, which treats the similar commit message as an exemplar and leverages it to guide the neural network model to generate an accurate commit message. We perform extensive experiments and the results confirm the effectiveness of our model.

Much of our experiments are designed to uncover the cause(s) and effect(s) behind a data generating mechanism (i.e., phenomenon) we happen to be interested in. Uncovering such relationships allows us to identify the true working of a phenomenon and, most importantly, articulate a model that may enable us to further explore the phenomenon on hand and/or allow us to predict it accurately. Fundamentally, such models are likely to be derived via a causal approach (as opposed to an observational or empirical mean). In this approach, causal discovery is required to create a causal model, which can then be applied to infer the influence of interventions, and answer any hypothetical questions (i.e., in the form of What ifs? Etc.) that we might have. This paper builds a case for causal discovery and causal inference and contrasts that against traditional machine learning approaches; all from a civil and structural engineering perspective. More specifically, this paper outlines the key principles of causality and the most commonly used algorithms and packages for causal discovery and causal inference. Finally, this paper also presents a series of examples and case studies of how causal concepts can be adopted for our domain.

One of the latest self-supervised learning (SSL) methods, VICReg, showed a great performance both in the linear evaluation and the fine-tuning evaluation. However, VICReg is proposed in computer vision and it learns by pulling representations of random crops of an image while maintaining the representation space by the variance and covariance loss. However, VICReg would be ineffective on non-stationary time series where different parts/crops of input should be differently encoded to consider the non-stationarity. Another recent SSL proposal, Temporal Neighborhood Coding (TNC) is effective for encoding non-stationary time series. This study shows that a combination of a VICReg-style method and TNC is very effective for SSL on non-stationary time series, where a non-stationary seismic signal time series is used as an evaluation dataset.

With the growing availability of large-scale datasets, and the popularization of affordable storage and computational capabilities, the energy consumed by AI is becoming a growing concern. To address this issue, in recent years, studies have focused on demonstrating how AI energy efficiency can be improved by tuning the model training strategy. Nevertheless, how modifications applied to datasets can impact the energy consumption of AI is still an open question. To fill this gap, in this exploratory study, we evaluate if data-centric approaches can be utilized to improve AI energy efficiency. To achieve our goal, we conduct an empirical experiment, executed by considering 6 different AI algorithms, a dataset comprising 5,574 data points, and two dataset modifications (number of data points and number of features). Our results show evidence that, by exclusively conducting modifications on datasets, energy consumption can be drastically reduced (up to 92.16%), often at the cost of a negligible or even absent accuracy decline. As additional introductory results, we demonstrate how, by exclusively changing the algorithm used, energy savings up to two orders of magnitude can be achieved. In conclusion, this exploratory investigation empirically demonstrates the importance of applying data-centric techniques to improve AI energy efficiency. Our results call for a research agenda that focuses on data-centric techniques, to further enable and democratize Green AI.

The recent emergence of reinforcement learning has created a demand for robust statistical inference methods for the parameter estimates computed using these algorithms. Existing methods for statistical inference in online learning are restricted to settings involving independently sampled observations, while existing statistical inference methods in reinforcement learning (RL) are limited to the batch setting. The online bootstrap is a flexible and efficient approach for statistical inference in linear stochastic approximation algorithms, but its efficacy in settings involving Markov noise, such as RL, has yet to be explored. In this paper, we study the use of the online bootstrap method for statistical inference in RL. In particular, we focus on the temporal difference (TD) learning and Gradient TD (GTD) learning algorithms, which are themselves special instances of linear stochastic approximation under Markov noise. The method is shown to be distributionally consistent for statistical inference in policy evaluation, and numerical experiments are included to demonstrate the effectiveness of this algorithm at statistical inference tasks across a range of real RL environments.

Policy gradient methods have been frequently applied to problems in control and reinforcement learning with great success, yet existing convergence analysis still relies on non-intuitive, impractical and often opaque conditions. In particular, existing rates are achieved in limited settings, under strict regularity conditions. In this work, we establish explicit convergence rates of policy gradient methods, extending the convergence regime to weakly smooth policy classes with $L_2$ integrable gradient. We provide intuitive examples to illustrate the insight behind these new conditions. Notably, our analysis also shows that convergence rates are achievable for both the standard policy gradient and the natural policy gradient algorithms under these assumptions. Lastly we provide performance guarantees for the converged policies.

We propose a method for jointly estimating the 3D motion, 3D shape, and appearance of highly motion-blurred objects from a video. To this end, we model the blurred appearance of a fast moving object in a generative fashion by parametrizing its 3D position, rotation, velocity, acceleration, bounces, shape, and texture over the duration of a predefined time window spanning multiple frames. Using differentiable rendering, we are able to estimate all parameters by minimizing the pixel-wise reprojection error to the input video via backpropagating through a rendering pipeline that accounts for motion blur by averaging the graphics output over short time intervals. For that purpose, we also estimate the camera exposure gap time within the same optimization. To account for abrupt motion changes like bounces, we model the motion trajectory as a piece-wise polynomial, and we are able to estimate the specific time of the bounce at sub-frame accuracy. Experiments on established benchmark datasets demonstrate that our method outperforms previous methods for fast moving object deblurring and 3D reconstruction.

Multi-institutional efforts can facilitate training of deep MRI reconstruction models, albeit privacy risks arise during cross-site sharing of imaging data. Federated learning (FL) has recently been introduced to address privacy concerns by enabling distributed training without transfer of imaging data. Existing FL methods for MRI reconstruction employ conditional models to map from undersampled to fully-sampled acquisitions via explicit knowledge of the imaging operator. Since conditional models generalize poorly across different acceleration rates or sampling densities, imaging operators must be fixed between training and testing, and they are typically matched across sites. To improve generalization and flexibility in multi-institutional collaborations, here we introduce a novel method for MRI reconstruction based on Federated learning of Generative IMage Priors (FedGIMP). FedGIMP leverages a two-stage approach: cross-site learning of a generative MRI prior, and subject-specific injection of the imaging operator. The global MRI prior is learned via an unconditional adversarial model that synthesizes high-quality MR images based on latent variables. Specificity in the prior is preserved via a mapper subnetwork that produces site-specific latents. During inference, the prior is combined with subject-specific imaging operators to enable reconstruction, and further adapted to individual test samples by minimizing data-consistency loss. Comprehensive experiments on multi-institutional datasets clearly demonstrate enhanced generalization performance of FedGIMP against site-specific and federated methods based on conditional models, as well as traditional reconstruction methods.

Deep generative networks in recent years have reinforced the need for caution while consuming various modalities of digital information. One avenue of deepfake creation is aligned with injection and removal of tumors from medical scans. Failure to detect medical deepfakes can lead to large setbacks on hospital resources or even loss of life. This paper attempts to address the detection of such attacks with a structured case study. Specifically, we evaluate eight different machine learning algorithms, which including three conventional machine learning methods, support vector machine, random forest, decision tree, and five deep learning models, DenseNet121, DenseNet201, ResNet50, ResNet101, VGG19, on distinguishing between tampered and untampered images.For deep learning models, the five models are used for feature extraction, then fine-tune for each pre-trained model is performed. The findings of this work show near perfect accuracy in detecting instances of tumor injections and removals.

In this work we address the problem of finding feasible policies for Constrained Markov Decision Processes under probability one constraints. We argue that stationary policies are not sufficient for solving this problem, and that a rich class of policies can be found by endowing the controller with a scalar quantity, so called budget, that tracks how close the agent is to violating the constraint. We show that the minimal budget required to act safely can be obtained as the smallest fixed point of a Bellman-like operator, for which we analyze its convergence properties. We also show how to learn this quantity when the true kernel of the Markov decision process is not known, while providing sample-complexity bounds. The utility of knowing this minimal budget relies in that it can aid in the search of optimal or near-optimal policies by shrinking down the region of the state space the agent must navigate. Simulations illustrate the different nature of probability one constraints against the typically used constraints in expectation.

Although deep learning (DL) has already become a state-of-the-art technology for various data processing tasks, data security and computational overload problems often arise due to their high data and computational power dependency. To solve this problem, quantum deep learning (QDL) and distributed deep learning (DDL) has emerged to complement existing DL methods. Furthermore, a quantum distributed deep learning (QDDL) technique that combines and maximizes these advantages is getting attention. This paper compares several model structures for QDDL and discusses their possibilities and limitations to leverage QDDL for some representative application scenarios.

Clustering algorithms partition a dataset into groups of similar points. The primary contribution of this article is the Multiscale Spatially-Regularized Diffusion Learning (M-SRDL) clustering algorithm, which uses spatially-regularized diffusion distances to efficiently and accurately learn multiple scales of latent structure in hyperspectral images. The M-SRDL clustering algorithm extracts clusterings at many scales from a hyperspectral image and outputs these clusterings' variation of information-barycenter as an exemplar for all underlying cluster structure. We show that incorporating spatial regularization into a multiscale clustering framework results in smoother and more coherent clusters when applied to hyperspectral data, yielding more accurate clustering labels.

Federated learning is an emerging research paradigm for enabling collaboratively training deep learning models without sharing patient data. However, the data from different institutions are usually heterogeneous across institutions, which may reduce the performance of models trained using federated learning. In this study, we propose a novel heterogeneity-aware federated learning method, SplitAVG, to overcome the performance drops from data heterogeneity in federated learning. Unlike previous federated methods that require complex heuristic training or hyper parameter tuning, our SplitAVG leverages the simple network split and feature map concatenation strategies to encourage the federated model training an unbiased estimator of the target data distribution. We compare SplitAVG with seven state-of-the-art federated learning methods, using centrally hosted training data as the baseline on a suite of both synthetic and real-world federated datasets. We find that the performance of models trained using all the comparison federated learning methods degraded significantly with the increasing degrees of data heterogeneity. In contrast, SplitAVG method achieves comparable results to the baseline method under all heterogeneous settings, that it achieves 96.2% of the accuracy and 110.4% of the mean absolute error obtained by the baseline in a diabetic retinopathy binary classification dataset and a bone age prediction dataset, respectively, on highly heterogeneous data partitions. We conclude that SplitAVG method can effectively overcome the performance drops from variability in data distributions across institutions. Experimental results also show that SplitAVG can be adapted to different base networks and generalized to various types of medical imaging tasks.

Generative Flow Networks (GFlowNets) have been introduced as a method to sample a diverse set of candidates in an active learning context, with a training objective that makes them approximately sample in proportion to a given reward function. In this paper, we show a number of additional theoretical properties of GFlowNets. They can be used to estimate joint probability distributions and the corresponding marginal distributions where some variables are unspecified and, of particular interest, can represent distributions over composite objects like sets and graphs. GFlowNets amortize the work typically done by computationally expensive MCMC methods in a single but trained generative pass. They could also be used to estimate partition functions and free energies, conditional probabilities of supersets (supergraphs) given a subset (subgraph), as well as marginal distributions over all supersets (supergraphs) of a given set (graph). We introduce variations enabling the estimation of entropy and mutual information, sampling from a Pareto frontier, connections to reward-maximizing policies, and extensions to stochastic environments, continuous actions and modular energy functions.

The nonuniform quantization strategy for compressing neural networks usually achieves better performance than its counterpart, i.e., uniform strategy, due to its superior representational capacity. However, many nonuniform quantization methods overlook the complicated projection process in implementing the nonuniformly quantized weights/activations, which incurs non-negligible time and space overhead in hardware deployment. In this study, we propose Nonuniform-to-Uniform Quantization (N2UQ), a method that can maintain the strong representation ability of nonuniform methods while being hardware-friendly and efficient as the uniform quantization for model inference. We achieve this through learning the flexible in-equidistant input thresholds to better fit the underlying distribution while quantizing these real-valued inputs into equidistant output levels. To train the quantized network with learnable input thresholds, we introduce a generalized straight-through estimator (G-STE) for intractable backward derivative calculation w.r.t. threshold parameters. Additionally, we consider entropy preserving regularization to further reduce information loss in weight quantization. Even under this adverse constraint of imposing uniformly quantized weights and activations, our N2UQ outperforms state-of-the-art nonuniform quantization methods by 0.5~1.7 on ImageNet, demonstrating the contribution of N2UQ design. Code and models are available at: https://github.com/liuzechun/Nonuniform-to-Uniform-Quantization.

The long-tailed class distribution in visual recognition tasks poses great challenges for neural networks on how to handle the biased predictions between head and tail classes, i.e., the model tends to classify tail classes as head classes. While existing research focused on data resampling and loss function engineering, in this paper, we take a different perspective: the classification margins. We study the relationship between the margins and logits (classification scores) and empirically observe the biased margins and the biased logits are positively correlated. We propose MARC, a simple yet effective MARgin Calibration function to dynamically calibrate the biased margins for unbiased logits. We validate MARC through extensive experiments on common long-tailed benchmarks including CIFAR-LT, ImageNet-LT, Places-LT, and iNaturalist-LT. Experimental results demonstrate that our MARC achieves favorable results on these benchmarks. In addition, MARC is extremely easy to implement with just three lines of code. We hope this simple method will motivate people to rethink the biased margins and biased logits in long-tailed visual recognition.

Detection of small objects and objects far away in the scene is a major challenge in surveillance applications. Such objects are represented by small number of pixels in the image and lack sufficient details, making them difficult to detect using conventional detectors. In this work, an open-source framework called Slicing Aided Hyper Inference (SAHI) is proposed that provides a generic slicing aided inference and fine-tuning pipeline for small object detection. The proposed technique is generic in the sense that it can be applied on top of any available object detector without any fine-tuning. Experimental evaluations, using object detection baselines on the Visdrone and xView aerial object detection datasets show that the proposed inference method can increase object detection AP by 6.8%, 5.1% and 5.3% for FCOS, VFNet and TOOD detectors, respectively. Moreover, the detection accuracy can be further increased with a slicing aided fine-tuning, resulting in a cumulative increase of 12.7%, 13.4% and 14.5% AP in the same order. Proposed technique has been integrated with Detectron2, MMDetection and YOLOv5 models and it is publicly available at https://github.com/obss/sahi.git .

In this work, we study the deception of a Linear-Quadratic-Gaussian (LQG) agent by manipulating the cost signals. We show that a small falsification of the cost parameters will only lead to a bounded change in the optimal policy. The bound is linear on the amount of falsification the attacker can apply to the cost parameters. We propose an attack model where the attacker aims to mislead the agent into learning a `nefarious' policy by intentionally falsifying the cost parameters. We formulate the attack's problem as a convex optimization problem and develop necessary and sufficient conditions to check the achievability of the attacker's goal. We showcase the adversarial manipulation on two types of LQG learners: the batch RL learner and the other is the adaptive dynamic programming (ADP) learner. Our results demonstrate that with only 2.296% of falsification on the cost data, the attacker misleads the batch RL into learning the 'nefarious' policy that leads the vehicle to a dangerous position. The attacker can also gradually trick the ADP learner into learning the same `nefarious' policy by consistently feeding the learner a falsified cost signal that stays close to the actual cost signal. The paper aims to raise people's awareness of the security threats faced by RL-enabled control systems.

Recently, adapting the idea of self-supervised learning (SSL) on continuous speech has started gaining attention. SSL models pre-trained on a huge amount of unlabeled audio can generate general-purpose representations that benefit a wide variety of speech processing tasks. Despite their ubiquitous deployment, however, the potential privacy risks of these models have not been well investigated. In this paper, we present the first privacy analysis on several SSL speech models using Membership Inference Attacks (MIA) under black-box access. The experiment results show that these pre-trained models are vulnerable to MIA and prone to membership information leakage with high Area Under the Curve (AUC) in both utterance-level and speaker-level. Furthermore, we also conduct several ablation studies to understand the factors that contribute to the success of MIA.

The use of reinforcement learning (RL) in scientific applications, such as materials design and automated chemistry, is increasing. A major challenge, however, lies in fact that measuring the state of the system is often costly and time consuming in scientific applications, whereas policy learning with RL requires a measurement after each time step. In this work, we make the measurement costs explicit in the form of a costed reward and propose a framework that enables off-the-shelf deep RL algorithms to learn a policy for both selecting actions and determining whether or not to measure the current state of the system at each time step. In this way, the agents learn to balance the need for information with the cost of information. Our results show that when trained under this regime, the Dueling DQN and PPO agents can learn optimal action policies whilst making up to 50\% fewer state measurements, and recurrent neural networks can produce a greater than 50\% reduction in measurements. We postulate the these reduction can help to lower the barrier to applying RL to real-world scientific applications.

The goal of this paper is to investigate a control theoretic analysis of linear stochastic iterative algorithm and temporal difference (TD) learning. TD-learning is a linear stochastic iterative algorithm to estimate the value function of a given policy for a Markov decision process, which is one of the most popular and fundamental reinforcement learning algorithms. While there has been a series of successful works in theoretical analysis of TD-learning, it was not until recently that researchers found some guarantees on its statistical efficiency. In this paper, we propose a control theoretic finite-time analysis TD-learning, which exploits standard notions in linear system control communities. Therefore, the proposed work provides additional insights on TD-learning and reinforcement learning with simple concepts and analysis tools in control theory.

The goal of imitation learning is to mimic expert behavior from demonstrations, without access to an explicit reward signal. A popular class of approach infers the (unknown) reward function via inverse reinforcement learning (IRL) followed by maximizing this reward function via reinforcement learning (RL). The policies learned via these approaches are however very brittle in practice and deteriorate quickly even with small test-time perturbations due to compounding errors. We propose Imitation with Planning at Test-time (IMPLANT), a new meta-algorithm for imitation learning that utilizes decision-time planning to correct for compounding errors of any base imitation policy. In contrast to existing approaches, we retain both the imitation policy and the rewards model at decision-time, thereby benefiting from the learning signal of the two components. Empirically, we demonstrate that IMPLANT significantly outperforms benchmark imitation learning approaches on standard control environments and excels at zero-shot generalization when subject to challenging perturbations in test-time dynamics.

Representations of the world environment play a crucial role in artificial intelligence. It is often inefficient to conduct reasoning and inference directly in the space of raw sensory representations, such as pixel values of images. Representation learning allows us to automatically discover suitable representations from raw sensory data. For example, given raw sensory data, a deep neural network learns nonlinear representations at its hidden layers, which are subsequently used for classification at its output layer. This happens implicitly during training through minimizing a supervised or unsupervised loss. In this paper, we study the dynamics of such implicit nonlinear representation learning. We identify a pair of a new assumption and a novel condition, called the common model structure assumption and the data-architecture alignment condition. Under the common model structure assumption, the data-architecture alignment condition is shown to be sufficient for the global convergence and necessary for the global optimality. Moreover, our theory explains how and when increasing the network size does and does not improve the training behaviors in the practical regime. Our results provide practical guidance for designing a model structure: e.g., the common model structure assumption can be used as a justification for using a particular model structure instead of others. We also derive a new training framework, which satisfies the data-architecture alignment condition by automatically modifying any given training algorithm. Given a standard training algorithm, the framework running its modified version is empirically shown to maintain competitive test performances while providing global convergence guarantees for deep residual neural networks with convolutions, skip connections, and batch normalization with datasets, including MNIST, CIFAR-10, CIFAR-100, Semeion, KMNIST and SVHN.

During the training process, deep neural networks implicitly learn to represent the input data samples through a hierarchy of features, where the size of the hierarchy is determined by the number of layers. In this paper, we focus on enforcing the discriminative power of the high-level representations, that are typically learned by the deeper layers (closer to the output). To this end, we introduce a new loss term inspired by the Gini impurity, which is aimed at minimizing the entropy (increasing the discriminative power) of individual high-level features with respect to the class labels. Although our Gini loss induces highly-discriminative features, it does not ensure that the distribution of the high-level features matches the distribution of the classes. As such, we introduce another loss term to minimize the Kullback-Leibler divergence between the two distributions. We conduct experiments on two image classification data sets (CIFAR-100 and Caltech 101), considering multiple neural architectures ranging from convolutional networks (ResNet-17, ResNet-18, ResNet-50) to transformers (CvT). Our empirical results show that integrating our novel loss terms into the training objective consistently outperforms the models trained with cross-entropy alone, without increasing the inference time at all.

Machine Learning with Deep Neural Networks (DNNs) has become a successful tool in solving tasks across various fields of application. The success of DNNs is strongly connected to their high complexity in terms of the number of network layers or of neurons in each layer, which severely complicates to understand how DNNs solve their learned task. To improve the explainability of DNNs, we adapt methods from neuroscience because this field has a rich experience in analyzing complex and opaque systems. In this work, we draw inspiration from how neuroscience uses topographic maps to visualize the activity of the brain when it performs certain tasks. Transferring this approach to DNNs can help to visualize and understand their internal processes more intuitively, too. However, the inner structures of brains and DNNs differ substantially. Therefore, to be able to visualize activations of neurons in DNNs as topographic maps, we research techniques to layout the neurons in a two-dimensional space in which neurons of similar activity are in the vicinity of each other. In this work, we introduce and compare different methods to obtain a topographic layout of the neurons in a network layer. Moreover, we demonstrate how to use the resulting topographic activation maps to identify errors or encoded biases in DNNs or data sets. Our novel visualization technique improves the transparency of DNN-based algorithmic decision-making systems and is accessible to a broad audience because topographic maps are intuitive to interpret without expert-knowledge in Machine Learning.

We introduce a novel method for identifying the mass composition of ultra-high-energy cosmic rays using deep learning. The key idea of the method is to use a chain of two neural networks. The first network predicts the type of a primary particle for individual events, while the second infers the mass composition of an ensemble of events. We apply this method to the Monte-Carlo data for the Telescope Array Surface Detectors readings, on which it yields an unprecedented low error of 7% for 4-component approximation. We also discuss the problems of applying the developed method to the experimental data, and the way they can be resolved.

In traditional robot exploration methods, the robot usually does not have prior biases about the environment it is exploring. Thus the robot assigns equal importance to the goals which leads to insufficient exploration efficiency. Alternative, often a hand-tuned policy is used to tweak the value of goals. In this paper, we present a method to learn how "good" some states are, measured by the state value function, to provide a hint for the robot to make exploration decisions. We propose to learn state value functions from previous offline collected datasets and then transfer and improve the value function during testing in a new environment. Moreover, the environments usually have very few and even no extrinsic reward or feedback for the robot. Therefore in this work, we also tackle the problem of sparse extrinsic rewards from the environments. We design several intrinsic rewards to encourage the robot to obtain more information during exploration. These reward functions then become the building blocks of the state value functions. We test our method on challenging subterranean and urban environments. To the best of our knowledge, this work for the first time demonstrates value function prediction with previous collected datasets to help exploration in challenging subterranean environments.

In the last few years, quantum computing has experienced a growth spurt. One exciting avenue of quantum computing is quantum machine learning (QML) which can exploit the high dimensional Hilbert space to learn richer representations from limited data and thus can efficiently solve complex learning tasks. Despite the increased interest in QML, there have not been many studies that discuss the security aspects of QML. In this work, we explored the possible future applications of QML in the hardware security domain. We also expose the security vulnerabilities of QML and emerging attack models, and corresponding countermeasures.

We introduce a new paradigm for single-channel target source separation where the sources of interest can be distinguished using non-mutually exclusive concepts (e.g., loudness, gender, language, spatial location, etc). Our proposed heterogeneous separation framework can seamlessly leverage datasets with large distribution shifts and learn cross-domain representations under a variety of concepts used as conditioning. Our experiments show that training separation models with heterogeneous conditions facilitates the generalization to new concepts with unseen out-of-domain data while also performing substantially higher than single-domain specialist models. Notably, such training leads to more robust learning of new harder source separation discriminative concepts and can yield improvements over permutation invariant training with oracle source selection. We analyze the intrinsic behavior of source separation training with heterogeneous metadata and propose ways to alleviate emerging problems with challenging separation conditions. We release the collection of preparation recipes for all datasets used to further promote research towards this challenging task.

We aim to provably complete a sparse and highly-missing tensor in the presence of covariate information along tensor modes. Our motivation comes from online advertising where users click-through-rates (CTR) on ads over various devices form a CTR tensor that has about 96% missing entries and has many zeros on non-missing entries, which makes the standalone tensor completion method unsatisfactory. Beside the CTR tensor, additional ad features or user characteristics are often available. In this paper, we propose Covariate-assisted Sparse Tensor Completion (COSTCO) to incorporate covariate information for the recovery of the sparse tensor. The key idea is to jointly extract latent components from both the tensor and the covariate matrix to learn a synthetic representation. Theoretically, we derive the error bound for the recovered tensor components and explicitly quantify the improvements on both the reveal probability condition and the tensor recovery accuracy due to covariates. Finally, we apply COSTCO to an advertisement dataset consisting of a CTR tensor and ad covariate matrix, leading to 23% accuracy improvement over the baseline. An important by-product is that ad latent components from COSTCO reveal interesting ad clusters, which are useful for better ad targeting.

With the surge in the number of hyperparameters and training times of modern machine learning models, hyperparameter tuning is becoming increasingly expensive. However, after assessing 40 tuning methods systematically, we find that each faces certain limitations. In particular, methods that speed up tuning via knowledge transfer typically require the final performance of hyperparameters and do not focus on low-fidelity information. As we demonstrate empirically, this common practice is suboptimal and can incur an unnecessary use of resources. It is more cost-efficient to instead leverage low-fidelity tuning observations to measure inter-task similarity and transfer knowledge from existing to new tasks accordingly. However, performing multi-fidelity tuning comes with its own challenges in the transfer setting: the noise in additional observations and the need for performance forecasting. Therefore, we propose and conduct a thorough analysis of a multi-task multi-fidelity Bayesian optimization framework, which leads to the best instantiation--amortized auto-tuning (AT2). We further present an offline-computed 27-task hyperparameter recommendation (HyperRec) database to serve the community. Extensive experiments on HyperRec and other real-world databases illustrate the effectiveness of our AT2 method.

The convolutional neural network has achieved great success in fulfilling computer vision tasks despite large computation overhead against efficient deployment. Structured (channel) pruning is usually applied to reduce the model redundancy while preserving the network structure, such that the pruned network can be easily deployed in practice. However, existing structured pruning methods require hand-crafted rules which may lead to tremendous pruning space. In this paper, we introduce Differentiable Annealing Indicator Search (DAIS) that leverages the strength of neural architecture search in the channel pruning and automatically searches for the effective pruned model with given constraints on computation overhead. Specifically, DAIS relaxes the binarized channel indicators to be continuous and then jointly learns both indicators and model parameters via bi-level optimization. To bridge the non-negligible discrepancy between the continuous model and the target binarized model, DAIS proposes an annealing-based procedure to steer the indicator convergence towards binarized states. Moreover, DAIS designs various regularizations based on a priori structural knowledge to control the pruning sparsity and to improve model performance. Experimental results show that DAIS outperforms state-of-the-art pruning methods on CIFAR-10, CIFAR-100, and ImageNet.

Conventional magneto-static finite element analysis of electrical machine design is time-consuming and computationally expensive. Since each machine topology has a distinct set of parameters, design optimization is commonly performed independently. This paper presents a novel method for predicting Key Performance Indicators (KPIs) of differently parameterized electrical machine topologies at the same time by mapping a high dimensional integrated design parameters in a lower dimensional latent space using a variational autoencoder. After training, via a latent space, the decoder and multi-layer neural network will function as meta-models for sampling new designs and predicting associated KPIs, respectively. This enables parameter-based concurrent multi-topology optimization.

A two-stage online dereverberation algorithm for hearing devices is presented in this paper. The approach combines a multi-channel multi-frame linear filtering approach with a single-channel single-frame post-filter. Both components rely on power spectral density (PSD) estimates provided by deep neural networks (DNNs). This contribution extends our prior work, which shows that directly optimizing for a criterion at the output of the multi-channel linear filtering stage results in a more efficient dereverberation, as compared to placing the criterion at the output of the DNN to optimize the PSD estimation. In the present work, we show that the dereverberation performance of the proposed first stage particularly improves the early-to-mid reverberation ratio if trained end-to-end. We thus argue that it can be combined with a post-filtering stage which benefits from the early-to-mid ratio improvement and is consequently able to efficiently suppress the residual late reverberation. This proposed two stage procedure is shown to be both very effective in terms of dereverberation performance and computational demands. Furthermore, the proposed system can be adapted to the needs of different types of hearing-device users by controlling the amount of reduction of early reflections. The proposed system outperforms the previously proposed end-to-end DNN-supported linear filtering algorithm, as well as other traditional approaches, based on an evaluation using the noise-free version of the WHAMR! dataset.

Most real optimization problems are defined over a mixed search space where the variables are both discrete and continuous. In engineering applications, the objective function is typically calculated with a numerically costly black-box simulation.General mixed and costly optimization problems are therefore of a great practical interest, yet their resolution remains in a large part an open scientific question. In this article, costly mixed problems are approached through Gaussian processes where the discrete variables are relaxed into continuous latent variables. The continuous space is more easily harvested by classical Bayesian optimization techniques than a mixed space would. Discrete variables are recovered either subsequently to the continuous optimization, or simultaneously with an additional continuous-discrete compatibility constraint that is handled with augmented Lagrangians. Several possible implementations of such Bayesian mixed optimizers are compared. In particular, the reformulation of the problem with continuous latent variables is put in competition with searches working directly in the mixed space. Among the algorithms involving latent variables and an augmented Lagrangian, a particular attention is devoted to the Lagrange multipliers for which a local and a global estimation techniques are studied. The comparisons are based on the repeated optimization of three analytical functions and a beam design problem.

When dealing with a series of imminent issues, humans can naturally concentrate on a subset of these concerning issues by prioritizing them according to their contributions to motivational indices, e.g., the probability of winning a game. This idea of concentration offers insights into reinforcement learning of sophisticated Large-scale Multi-Agent Systems (LMAS) participated by hundreds of agents. In such an LMAS, each agent receives a long series of entity observations at each step, which can overwhelm existing aggregation networks such as graph attention networks and cause inefficiency. In this paper, we propose a concentration network called ConcNet. First, ConcNet scores the observed entities considering several motivational indices, e.g., expected survival time and state value of the agents, and then ranks, prunes, and aggregates the encodings of observed entities to extract features. Second, distinct from the well-known attention mechanism, ConcNet has a unique motivational subnetwork to explicitly consider the motivational indices when scoring the observed entities. Furthermore, we present a concentration policy gradient architecture that can learn effective policies in LMAS from scratch. Extensive experiments demonstrate that the presented architecture has excellent scalability and flexibility, and significantly outperforms existing methods on LMAS benchmarks.  ( 2 min )

Multimodal variational autoencoders (VAEs) have shown promise as efficient generative models for weakly-supervised data. Yet, despite their advantage of weak supervision, they exhibit a gap in generative quality compared to unimodal VAEs, which are completely unsupervised. In an attempt to explain this gap, we uncover a fundamental limitation that applies to a large family of mixture-based multimodal VAEs. We prove that the sub-sampling of modalities enforces an undesirable upper bound on the multimodal ELBO and thereby limits the generative quality of the respective models. Empirically, we showcase the generative quality gap on both synthetic and real data and present the tradeoffs between different variants of multimodal VAEs. We find that none of the existing approaches fulfills all desired criteria of an effective multimodal generative model when applied on more complex datasets than those used in previous benchmarks. In summary, we identify, formalize, and validate fundamental limitations of VAE-based approaches for modeling weakly-supervised data and discuss implications for real-world applications.

It's better to say "I can't answer" than to answer incorrectly. This selective prediction ability is crucial for NLP systems to be reliably deployed in real-world applications. Prior work has shown that existing selective prediction techniques fail to perform well, especially in the out-of-domain setting. In this work, we propose a method that improves probability estimates of models by calibrating them using prediction confidence and difficulty score of instances. Using these two signals, we first annotate held-out instances and then train a calibrator to predict the likelihood of correctness of the model's prediction. We instantiate our method with Natural Language Inference (NLI) and Duplicate Detection (DD) tasks and evaluate it in both In-Domain (IID) and Out-of-Domain (OOD) settings. In (IID, OOD) settings, we show that the representations learned by our calibrator result in an improvement of (15.81%, 5.64%) and (6.19%, 13.9%) over 'MaxProb' -- a selective prediction baseline -- on NLI and DD tasks respectively.

Modern deep learning training procedures rely on model regularization techniques such as data augmentation methods, which generate training samples that increase the diversity of data and richness of label information. A popular recent method, mixup, uses convex combinations of pairs of original samples to generate new samples. However, as we show in our experiments, mixup can produce undesirable synthetic samples, where the data is sampled off the manifold and can contain incorrect labels. We propose $\zeta$-mixup, a generalization of mixup with provably and demonstrably desirable properties that allows convex combinations of $N \geq 2$ samples, leading to more realistic and diverse outputs that incorporate information from $N$ original samples by using a $p$-series interpolant. We show that, compared to mixup, $\zeta$-mixup better preserves the intrinsic dimensionality of the original datasets, which is a desirable property for training generalizable models. Furthermore, we show that our implementation of $\zeta$-mixup is faster than mixup, and extensive evaluation on controlled synthetic and 24 real-world natural and medical image classification datasets shows that $\zeta$-mixup outperforms mixup and traditional data augmentation techniques.

Earth observation from satellites offers the possibility to monitor our planet with unprecedented accuracy. Radiative transfer models (RTMs) encode the energy transfer through the atmosphere, and are used to model and understand the Earth system, as well as to estimate the parameters that describe the status of the Earth from satellite observations by inverse modeling. However, performing inference over such simulators is a challenging problem. RTMs are nonlinear, non-differentiable and computationally costly codes, which adds a high level of difficulty in inference. In this paper, we introduce two computational techniques to infer not only point estimates of biophysical parameters but also their joint distribution. One of them is based on a variational autoencoder approach and the second one is based on a Monte Carlo Expectation Maximization (MCEM) scheme. We compare and discuss benefits and drawbacks of each approach. We also provide numerical comparisons in synthetic simulations and the real PROSAIL model, a popular RTM that combines land vegetation leaf and canopy modeling. We analyze the performance of the two approaches for modeling and inferring the distribution of three key biophysical parameters for quantifying the terrestrial biosphere.

The idea of "data justice" is of recent academic vintage. It has arisen over the past decade in Anglo-European research institutions as an attempt to bring together a critique of the power dynamics that underlie accelerating trends of datafication with a normative commitment to the principles of social justice-a commitment to the achievement of a society that is equitable, fair, and capable of confronting the root causes of injustice.However, despite the seeming novelty of such a data justice pedigree, this joining up of the critique of the power imbalances that have shaped the digital and "big data" revolutions with a commitment to social equity and constructive societal transformation has a deeper historical, and more geographically diverse, provenance. As the stories of the data justice initiatives, activism, and advocacy contained in this volume well evidence, practices of data justice across the globe have, in fact, largely preceded the elaboration and crystallisation of the idea of data justice in contemporary academic discourse. In telling these data justice stories, we hope to provide the reader with two interdependent tools of data justice thinking: First, we aim to provide the reader with the critical leverage needed to discern those distortions and malformations of data justice that manifest in subtle and explicit forms of power, domination, and coercion. Second, we aim to provide the reader with access to the historically effective forms of normativity and ethical insight that have been marshalled by data justice activists and advocates as tools of societal transformation-so that these forms of normativity and insight can be drawn on, in turn, as constructive resources to spur future transformative data justice practices.

Learning a new language involves constantly comparing speech productions with reference productions from the environment. Early in speech acquisition, children make articulatory adjustments to match their caregivers' speech. Grownup learners of a language tweak their speech to match the tutor reference. This paper proposes a method to synthetically generate correct pronunciation feedback given incorrect production. Furthermore, our aim is to generate the corrected production while maintaining the speaker's original voice. The system prompts the user to pronounce a phrase. The speech is recorded, and the samples associated with the inaccurate phoneme are masked with zeros. This waveform serves as an input to a speech generator, implemented as a deep learning inpainting system with a U-net architecture, and trained to output a reconstructed speech. The training set is composed of unimpaired proper speech examples, and the generator is trained to reconstruct the original proper speech. We evaluated the performance of our system on phoneme replacement of minimal pair words of English as well as on children with pronunciation disorders. Results suggest that human listeners slightly prefer our generated speech over a smoothed replacement of the inaccurate phoneme with a production of a different speaker.

Self-attention is a key enabler of state-of-art accuracy for various transformer-based Natural Language Processing models. This attention mechanism calculates a correlation score for each word with respect to the other words in a sentence. Commonly, only a small subset of words highly correlates with the word under attention, which is only determined at runtime. As such, a significant amount of computation is inconsequential due to low attention scores and can potentially be pruned. The main challenge is finding the threshold for the scores below which subsequent computation will be inconsequential. Although such a threshold is discrete, this paper formulates its search through a soft differentiable regularizer integrated into the loss function of the training. This formulation piggy backs on the back-propagation training to analytically co-optimize the threshold and the weights simultaneously, striking a formally optimal balance between accuracy and computation pruning. To best utilize this mathematical innovation, we devise a bit-serial architecture, dubbed LeOPArd, for transformer language models with bit-level early termination microarchitectural mechanism. We evaluate our design across 43 back-end tasks for MemN2N, BERT, ALBERT, GPT-2, and Vision transformer models. Post-layout results show that, on average, LeOPArd yields 1.9x and 3.9x speedup and energy reduction, respectively, while keeping the average accuracy virtually intact (<0.2% degradation)

The rise of deep neural networks has led to several breakthroughs for semantic segmentation. In spite of this, a model trained on source domain often fails to work properly in new challenging domains, that is directly concerned with the generalization capability of the model. In this paper, we present a novel memory-guided domain generalization method for semantic segmentation based on meta-learning framework. Especially, our method abstracts the conceptual knowledge of semantic classes into categorical memory which is constant beyond the domains. Upon the meta-learning concept, we repeatedly train memory-guided networks and simulate virtual test to 1) learn how to memorize a domain-agnostic and distinct information of classes and 2) offer an externally settled memory as a class-guidance to reduce the ambiguity of representation in the test data of arbitrary unseen domain. To this end, we also propose memory divergence and feature cohesion losses, which encourage to learn memory reading and update processes for category-aware domain generalization. Extensive experiments for semantic segmentation demonstrate the superior generalization capability of our method over state-of-the-art works on various benchmarks.

Class-incremental learning (CIL) has been widely studied under the setting of starting from a small number of classes (base classes). Instead, we explore an understudied real-world setting of CIL that starts with a strong model pre-trained on a large number of base classes. We hypothesize that a strong base model can provide a good representation for novel classes and incremental learning can be done with small adaptations. We propose a 2-stage training scheme, i) feature augmentation -- cloning part of the backbone and fine-tuning it on the novel data, and ii) fusion -- combining the base and novel classifiers into a unified classifier. Experiments show that the proposed method significantly outperforms state-of-the-art CIL methods on the large-scale ImageNet dataset (e.g. +10% overall accuracy than the best). We also propose and analyze understudied practical CIL scenarios, such as base-novel overlap with distribution shift. Our proposed method is robust and generalizes to all analyzed CIL settings.

Graph Neural Networks (GNNs) aim to extend deep learning techniques to graph data and have achieved significant progress in graph analysis tasks (e.g., node classification) in recent years. However, similar to other deep neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), GNNs behave like a black box with their details hidden from model developers and users. It is therefore difficult to diagnose possible errors of GNNs. Despite many visual analytics studies being done on CNNs and RNNs, little research has addressed the challenges for GNNs. This paper fills the research gap with an interactive visual analysis tool, GNNLens, to assist model developers and users in understanding and analyzing GNNs. Specifically, Parallel Sets View and Projection View enable users to quickly identify and validate error patterns in the set of wrong predictions; Graph View and Feature Matrix View offer a detailed analysis of individual nodes to assist users in forming hypotheses about the error patterns. Since GNNs jointly model the graph structure and the node features, we reveal the relative influences of the two types of information by comparing the predictions of three models: GNN, Multi-Layer Perceptron (MLP), and GNN Without Using Features (GNNWUF). Two case studies and interviews with domain experts demonstrate the effectiveness of GNNLens in facilitating the understanding of GNN models and their errors.

Feature selection is an important data preprocessing in data mining and machine learning which can be used to reduce the feature dimension without deteriorating model's performance. Since obtaining annotated data is laborious or even infeasible in many cases, unsupervised feature selection is more practical in reality. Though lots of methods for unsupervised feature selection have been proposed, these methods select features independently, thus it is no guarantee that the group of selected features is optimal. What's more, the number of selected features must be tuned carefully to obtain a satisfactory result. To tackle these problems, we propose a joint adaptive graph and structured sparsity regularization unsupervised feature selection (JASFS) method in this paper, in which a $l_{2,0}$-norm regularization term with respect to transformation matrix is imposed in the manifold learning for feature selection, and a graph regularization term is incorporated into the learning model to learn the local geometric structure of data adaptively. An efficient and simple iterative algorithm is designed to solve the proposed optimization problem with the analysis of computational complexity. After optimized, a subset of optimal features will be selected in group, and the number of selected features will be determined automatically. Experimental results on eight benchmarks demonstrate the effectiveness and efficiency of the proposed method compared with several state-of-the-art approaches.

System design tools are often only available as input-output blackboxes: for a given design as input they compute an output representing system behavior. Blackboxes are intended to be run in the forward direction. This paper presents a new method of solving the inverse design problem namely, given requirements or constraints on output, find an input that also optimizes an objective function. This problem is challenging for several reasons. First, blackboxes are not designed to be run in reverse. Second, inputs and outputs can be discrete and continuous. Third, finding designs concurrently satisfying a set of requirements is hard because designs satisfying individual requirements may conflict with each other. Fourth, blackbox evaluations can be expensive. Finally, blackboxes can sometimes fail to produce an output. This paper presents CNMA, a new method of solving the inverse problem that overcomes these challenges. CNMA tries to sample only the part of the design space relevant to solving the problem, leveraging the power of neural networks, Mixed Integer Linear Programs, and a new learning-from-failure feedback loop. The paper also presents a parallel version of CNMA that improves the efficiency and quality of solutions over the sequential version, and tries to steer it away from local optima. CNMA's performance is evaluated against conventional optimization methods for seven nonlinear design problems of 8 (two problems), 10, 15, 36 and 60 real-valued dimensions and one with 186 binary dimensions. Conventional methods evaluated are off-the-shelf implementations of Bayesian Optimization with Gaussian Processes, Nelder Mead and Random Search. The first two do not solve problems that are high-dimensional, have discrete and continuous variables or whose blackboxes can fail to return values. CNMA solves all problems, and surpasses the performance of conventional methods by up to 87%.

DeepTensor is a computationally efficient framework for low-rank decomposition of matrices and tensors using deep generative networks. We decompose a tensor as the product of low-rank tensor factors (e.g., a matrix as the outer product of two vectors), where each low-rank tensor is generated by a deep network (DN) that is trained in a self-supervised manner to minimize the mean-squared approximation error. Our key observation is that the implicit regularization inherent in DNs enables them to capture nonlinear signal structures (e.g., manifolds) that are out of the reach of classical linear methods like the singular value decomposition (SVD) and principal component analysis (PCA). Furthermore, in contrast to the SVD and PCA, whose performance deteriorates when the tensor's entries deviate from additive white Gaussian noise, we demonstrate that the performance of DeepTensor is robust to a wide range of distributions. We validate that DeepTensor is a robust and computationally efficient drop-in replacement for the SVD, PCA, nonnegative matrix factorization (NMF), and similar decompositions by exploring a range of real-world applications, including hyperspectral image denoising, 3D MRI tomography, and image classification. In particular, DeepTensor offers a 6dB signal-to-noise ratio improvement over standard denoising methods for signals corrupted by Poisson noise and learns to decompose 3D tensors 60 times faster than a single DN equipped with 3D convolutions.

Improving the user's hearing ability to understand speech in noisy environments is critical to the development of hearing aid (HA) devices. For this, it is important to derive a metric that can fairly predict speech intelligibility for HA users. A straightforward approach is to conduct a subjective listening test and use the test results as an evaluation metric. However, conducting large-scale listening tests is time-consuming and expensive. Therefore, several evaluation metrics were derived as surrogates for subjective listening test results. In this study, we propose a multi-branched speech intelligibility prediction model (MBI-Net), for predicting the subjective intelligibility scores of HA users. MBI-Net consists of two branches of models, with each branch consisting of a hearing loss model, a cross-domain feature extraction module, and a speech intelligibility prediction model, to process speech signals from one channel. The outputs of the two branches are fused through a linear layer to obtain predicted speech intelligibility scores. Experimental results confirm the effectiveness of MBI-Net, which produces higher prediction scores than the baseline system in Track 1 and Track 2 on the Clarity Prediction Challenge 2022 dataset.

Context: Mobile app reviews written by users on app stores or social media are significant resources for app developers.Analyzing app reviews have proved to be useful for many areas of software engineering (e.g., requirement engineering, testing). Automatic classification of app reviews requires extensive efforts to manually curate a labeled dataset. When the classification purpose changes (e.g. identifying bugs versus usability issues or sentiment), new datasets should be labeled, which prevents the extensibility of the developed models for new desired classes/tasks in practice. Recent pre-trained neural language models (PTM) are trained on large corpora in an unsupervised manner and have found success in solving similar Natural Language Processing problems. However, the applicability of PTMs is not explored for app review classification Objective: We investigate the benefits of PTMs for app review classification compared to the existing models, as well as the transferability of PTMs in multiple settings. Method: We empirically study the accuracy and time efficiency of PTMs compared to prior approaches using six datasets from literature. In addition, we investigate the performance of the PTMs trained on app reviews (i.e. domain-specific PTMs) . We set up different studies to evaluate PTMs in multiple settings: binary vs. multi-class classification, zero-shot classification (when new labels are introduced to the model), multi-task setting, and classification of reviews from different resources. The datasets are manually labeled app review datasets from Google Play Store, Apple App Store, and Twitter data. In all cases, Micro and Macro Precision, Recall, and F1-scores will be used and we will report the time required for training and prediction with the models.

We present a mean-variance policy iteration (MVPI) framework for risk-averse control in a discounted infinite horizon MDP optimizing the variance of a per-step reward random variable. MVPI enjoys great flexibility in that any policy evaluation method and risk-neutral control method can be dropped in for risk-averse control off the shelf, in both on- and off-policy settings. This flexibility reduces the gap between risk-neutral control and risk-averse control and is achieved by working on a novel augmented MDP directly. We propose risk-averse TD3 as an example instantiating MVPI, which outperforms vanilla TD3 and many previous risk-averse control methods in challenging Mujoco robot simulation tasks under a risk-aware performance metric. This risk-averse TD3 is the first to introduce deterministic policies and off-policy learning into risk-averse reinforcement learning, both of which are key to the performance boost we show in Mujoco domains.

We study the basic operation of set union in the global model of differential privacy. In this problem, we are given a universe $U$ of items, possibly of infinite size, and a database $D$ of users. Each user $i$ contributes a subset $W_i \subseteq U$ of items. We want an ($\epsilon$,$\delta$)-differentially private algorithm which outputs a subset $S \subset \cup_i W_i$ such that the size of $S$ is as large as possible. The problem arises in countless real world applications; it is particularly ubiquitous in natural language processing (NLP) applications as vocabulary extraction. For example, discovering words, sentences, $n$-grams etc., from private text data belonging to users is an instance of the set union problem. Known algorithms for this problem proceed by collecting a subset of items from each user, taking the union of such subsets, and disclosing the items whose noisy counts fall above a certain threshold. Crucially, in the above process, the contribution of each individual user is always independent of the items held by other users, resulting in a wasteful aggregation process, where some item counts happen to be way above the threshold. We deviate from the above paradigm by allowing users to contribute their items in a $\textit{dependent fashion}$, guided by a $\textit{policy}$. In this new setting ensuring privacy is significantly delicate. We prove that any policy which has certain $\textit{contractive}$ properties would result in a differentially private algorithm. We design two new algorithms, one using Laplace noise and other Gaussian noise, as specific instances of policies satisfying the contractive properties. Our experiments show that the new algorithms significantly outperform previously known mechanisms for the problem.

Multi-head self-attention forms the core of Transformer networks. However, their quadratically growing complexity with respect to the input sequence length impedes their deployment on resource-constrained edge devices. We address this challenge by proposing a dynamic pruning method, which exploits the temporal stability of data across tokens to reduce inference cost. The threshold-based method only retains significant differences between the subsequent tokens, effectively reducing the number of multiply-accumulates, as well as the internal tensor data sizes. The approach is evaluated on the Google Speech Commands Dataset for keyword spotting, and the performance is compared against the baseline Keyword Transformer. Our experiments show that we can reduce ~80% of operations while maintaining the original 98.4% accuracy. Moreover, a reduction of ~87-94% operations can be achieved when only degrading the accuracy by 1-4%, speeding up the multi-head self-attention inference by a factor of ~7.5-16.

Contrastive learning has recently seen tremendous success in self-supervised learning. So far, however, it is largely unclear why the learned representations generalize so effectively to a large variety of downstream tasks. We here prove that feedforward models trained with objectives belonging to the commonly used InfoNCE family learn to implicitly invert the underlying generative model of the observed data. While the proofs make certain statistical assumptions about the generative model, we observe empirically that our findings hold even if these assumptions are severely violated. Our theory highlights a fundamental connection between contrastive learning, generative modeling, and nonlinear independent component analysis, thereby furthering our understanding of the learned representations as well as providing a theoretical foundation to derive more effective contrastive losses.

We present new policy mirror descent (PMD) methods for solving reinforcement learning (RL) problems with either strongly convex or general convex regularizers. By exploring the structural properties of these overall highly nonconvex problems we show that the PMD methods exhibit fast linear rate of convergence to the global optimality. We develop stochastic counterparts of these methods, and establish an ${\cal O}(1/\epsilon)$ (resp., ${\cal O}(1/\epsilon^2)$) sampling complexity for solving these RL problems with strongly (resp., general) convex regularizers using different sampling schemes, where $\epsilon$ denote the target accuracy. We further show that the complexity for computing the gradients of these regularizers, if necessary, can be bounded by ${\cal O}\{(\log_\gamma \epsilon) [(1-\gamma)L/\mu]^{1/2}\log (1/\epsilon)\}$ (resp., ${\cal O} \{(\log_\gamma \epsilon ) (L/\epsilon)^{1/2}\}$)for problems with strongly (resp., general) convex regularizers. Here $\gamma$ denotes the discounting factor. To the best of our knowledge, these complexity bounds, along with our algorithmic developments, appear to be new in both optimization and RL literature. The introduction of these convex regularizers also greatly expands the flexibility and applicability of RL models.

Automated short answer grading (ASAG) has gained attention in education as a means to scale educational tasks to the growing number of students. Recent progress in Natural Language Processing and Machine Learning has largely influenced the field of ASAG, of which we survey the recent research advancements. We complement previous surveys by providing a comprehensive analysis of recently published methods that deploy deep learning approaches. In particular, we focus our analysis on the transition from hand engineered features to representation learning approaches, which learn representative features for the task at hand automatically from large corpora of data. We structure our analysis of deep learning methods along three categories: word embeddings, sequential models, and attention-based methods. Deep learning impacted ASAG differently than other fields of NLP, as we noticed that the learned representations alone do not contribute to achieve the best results, but they rather show to work in a complementary way with hand-engineered features. The best performance are indeed achieved by methods that combine the carefully hand-engineered features with the power of the semantic descriptions provided by the latest models, like transformers architectures. We identify challenges and provide an outlook on research direction that can be addressed in the future

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed the temporal and anterior lobes of hippocampus regions of brain get affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. The magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to obtain accurate diagnosis of SZ. This paper presents a comprehensive overview of studies conducted on automated diagnosis of SZ using MRI modalities. Main findings, various challenges, and future works in developing the automated SZ detection are described in this paper.

Visual recognition is recently learned via either supervised learning on human-annotated image-label data or language-image contrastive learning with webly-crawled image-text pairs. While supervised learning may result in a more discriminative representation, language-image pretraining shows unprecedented zero-shot recognition capability, largely due to the different properties of data sources and learning objectives. In this work, we introduce a new formulation by combining the two data sources into a common image-text-label space. In this space, we propose a new learning paradigm, called Unified Contrastive Learning (UniCL) with a single learning objective to seamlessly prompt the synergy of two data types. Extensive experiments show that our UniCL is an effective way of learning semantically rich yet discriminative representations, universally for image recognition in zero-shot, linear-probe, fully finetuning and transfer learning scenarios. Particularly, it attains gains up to 9.2% and 14.5% in average on zero-shot recognition benchmarks over the language-image contrastive learning and supervised learning methods, respectively. In linear probe setting, it also boosts the performance over the two methods by 7.3% and 3.4%, respectively. Our study also indicates that UniCL stand-alone is a good learner on pure image-label data, rivaling the supervised learning methods across three image classification datasets and two types of vision backbones, ResNet and Swin Transformer. Code is available at https://github.com/microsoft/UniCL.

The Epithelial Dysplasia (ED) is a tissue alteration commonly present in lesions preceding oral cancer, being its presence one of the most important factors in the progression toward carcinoma. This study proposes a method to design a low computational cost classification system to support the detection of dysplastic epithelia, contributing to reduce the variability of pathologist assessments. We employ a multilayer artificial neural network (MLP-ANN) and defining the regions of the epithelium to be assessed based on the knowledge of the pathologist. The performance of the proposed solution was statistically evaluated. The implemented MLP-ANN presented an average accuracy of 87%, with a variability much inferior to that obtained from three trained evaluators. Moreover, the proposed solution led to results which are very close to those obtained using a convolutional neural network (CNN) implemented by transfer learning, with 100 times less computational complexity. In conclusion, our results show that a simple neural network structure can lead to a performance equivalent to that of much more complex structures, which are routinely used in the literature.

Designing equivariance as an inductive bias into deep-nets has been a prominent approach to build effective models, e.g., a convolutional neural network incorporates translation equivariance. However, incorporating these inductive biases requires knowledge about the equivariance properties of the data, which may not be available, e.g., when encountering a new domain. To address this, we study how to discover interpretable equivariances from data. Specifically, we formulate this discovery process as an optimization problem over a model's parameter-sharing schemes. We propose to use the partition distance to empirically quantify the accuracy of the recovered equivariance. Also, we theoretically analyze the method for Gaussian data and provide a bound on the mean squared gap between the studied discovery scheme and the oracle scheme. Empirically, we show that the approach recovers known equivariances, such as permutations and shifts, on sum of numbers and spatially-invariant data.

Several modern applications require the integration of multiple large data matrices that have shared rows and/or columns. For example, cancer studies that integrate multiple omics platforms across multiple types of cancer, pan-omics pan-cancer analysis, have extended our knowledge of molecular heterogenity beyond what was observed in single tumor and single platform studies. However, these studies have been limited by available statistical methodology. We propose a flexible approach to the simultaneous factorization and decomposition of variation across such bidimensionally linked matrices, BIDIFAC+. This decomposes variation into a series of low-rank components that may be shared across any number of row sets (e.g., omics platforms) or column sets (e.g., cancer types). This builds on a growing literature for the factorization and decomposition of linked matrices, which has primarily focused on multiple matrices that are linked in one dimension (rows or columns) only. Our objective function extends nuclear norm penalization, is motivated by random matrix theory, gives an identifiable decomposition under relatively mild conditions, and can be shown to give the mode of a Bayesian posterior distribution. We apply BIDIFAC+ to pan-omics pan-cancer data from TCGA, identifying shared and specific modes of variability across 4 different omics platforms and 29 different cancer types.

Few-shot learning aims to transfer the knowledge acquired from training on a diverse set of tasks, from a given task distribution, to generalize to unseen tasks, from the same distribution, with a limited amount of labeled data. The underlying requirement for effective few-shot generalization is to learn a good representation of the task manifold. One way to encourage this is to preserve local neighborhoods in the feature space learned by the few-shot learner. To this end, we introduce the notion of interval bounds from the provably robust training literature to few-shot learning. The interval bounds are used to characterize neighborhoods around the training tasks. These neighborhoods can then be preserved by minimizing the distance between a task and its respective bounds. We further introduce a novel strategy to artificially form new tasks for training by interpolating between the available tasks and their respective interval bounds, to aid in cases with a scarcity of tasks. We apply our framework to both model-agnostic meta-learning as well as prototype-based metric-learning paradigms. The efficacy of our proposed approach is evident from the improved performance on several datasets from diverse domains in comparison to a sizable number of recent competitors.

Stress, anxiety, and nervousness are all high-risk health states in everyday life. Previously, stress levels were determined by speaking with people and gaining insight into what they had experienced recently or in the past. Typically, stress is caused by an incidence that occurred a long time ago, but sometimes it is triggered by unknown factors. This is a challenging and complex task, but recent research advances have provided numerous opportunities to automate it. The fundamental features of most of these techniques are electro dermal activity (EDA) and heart rate values (HRV). We utilized an accelerometer to measure body motions to solve this challenge. The proposed novel method employs a test that measures a subject's electrocardiogram (ECG), galvanic skin values (GSV), HRV values, and body movements in order to provide a low-cost and time-saving solution for detecting stress lifestyle disease in modern times using cyber physical systems. This study provides a new hybrid model for lifestyle disease classification that decreases execution time while picking the best collection of characteristics and increases classification accuracy. The developed approach is capable of dealing with the class imbalance problem by using WESAD (wearable stress and affect dataset) dataset. The new model uses the Grid search (GS) method to select an optimized set of hyper parameters, and it uses a combination of the Correlation coefficient based Recursive feature elimination (CoC-RFE) method for optimal feature selection and gradient boosting as an estimator to classify the dataset, which achieves high accuracy and helps to provide smart, accurate, and high-quality healthcare systems. To demonstrate the validity and utility of the proposed methodology, its performance is compared to those of other well-established machine learning models.

With the rapid advance of information technology, network systems have become increasingly complex and hence the underlying system dynamics are often unknown or difficult to characterize. Finding a good network control policy is of significant importance to achieve desirable network performance (e.g., high throughput or low delay). In this work, we consider using model-based reinforcement learning (RL) to learn the optimal control policy for queueing networks so that the average job delay (or equivalently the average queue backlog) is minimized. Traditional approaches in RL, however, cannot handle the unbounded state spaces of the network control problem. To overcome this difficulty, we propose a new algorithm, called Reinforcement Learning for Queueing Networks (RL-QN), which applies model-based RL methods over a finite subset of the state space, while applying a known stabilizing policy for the rest of the states. We establish that the average queue backlog under RL-QN with an appropriately constructed subset can be arbitrarily close to the optimal result. We evaluate RL-QN in dynamic server allocation, routing and switching problems. Simulation results show that RL-QN minimizes the average queue backlog effectively.

Probing Pre-trained Language Models (PLMs) using prompts has indirectly implied that language models (LMs) can be treated as knowledge bases. To this end, this phenomena has been effective especially when these LMs are fine-tuned towards not just data of a specific domain, but also to the style or linguistic pattern of the prompts themselves. We observe that, satisfying a particular linguistic pattern in prompts is an unsustainable constraint that unnecessarily lengthens the probing task, especially because, they are often manually designed and the range of possible prompt template patterns can vary depending on the prompting objective and domain. We therefore explore an idea of using a position-attention mechanism to capture positional information of each word in a prompt relative to the mask to be filled, hence avoiding the need to re-construct prompts when the prompts linguistic pattern changes. Using our approach, we demonstrate the ability of eliciting answers to rare prompt templates (in a case study on health outcome generation) such as Postfix and Mixed patterns whose missing information is respectively at the start and in multiple random places of the prompt. More so, using various biomedical PLMs, our approach consistently outperforms a baseline in which the default mask language model (MLM) representation is used to predict masked tokens.

Second-order semantic parsing with end-to-end mean-field inference has been shown good performance. In this work we aim to improve this method by modeling label correlations between adjacent arcs. However, direct modeling leads to memory explosion because second-order score tensors have sizes of $O(n^3L^2)$ ($n$ is the sentence length and $L$ is the number of labels), which is not affordable. To tackle this computational challenge, we leverage tensor decomposition techniques, and interestingly, we show that the large second-order score tensors have no need to be materialized during mean-field inference, thereby reducing the computational complexity from cubic to quadratic. We conduct experiments on SemEval 2015 Task 18 English datasets, showing the effectiveness of modeling label correlations. Our code is publicly available at https://github.com/sustcsonglin/mean-field-dep-parsing.

As an emerging technology, federated learning (FL) involves training machine learning models over distributed edge devices, which attracts sustained attention and has been extensively studied. However, the heterogeneity of client data severely degrades the performance of FL compared with that in centralized training. It causes the locally trained models of clients to move in different directions. On the one hand, it slows down or even stalls the global updates, leading to inefficient communication. On the other hand, it enlarges the distances between local models, resulting in an aggregated global model with poor performance. Fortunately, these shortcomings can be mitigated by reducing the angle between the directions that local models move in. Based on this fact, we propose FedCos, which reduces the directional inconsistency of local models by introducing a cosine-similarity penalty. It promotes the local model iterations towards an auxiliary global direction. Moreover, our approach is auto-adapt to various non-IID settings without an elaborate selection of hyperparameters. The experimental results show that FedCos outperforms the well-known baselines and can enhance them under a variety of FL scenes, including varying degrees of data heterogeneity, different number of participants, and cross-silo and cross-device settings. Besides, FedCos improves communication efficiency by 2 to 5 times. With the help of FedCos, multiple FL methods require significantly fewer communication rounds than before to obtain a model with comparable performance.

We introduce distributed NLI, a new NLU task with a goal to predict the distribution of human judgements for natural language inference. We show that by applying additional distribution estimation methods, namely, Monte Carlo (MC) Dropout, Deep Ensemble, Re-Calibration, and Distribution Distillation, models can capture human judgement distribution more effectively than the softmax baseline. We show that MC Dropout is able to achieve decent performance without any distribution annotations while Re-Calibration can give further improvements with extra distribution annotations, suggesting the value of multiple annotations for one example in modeling the distribution of human judgements. Despite these improvements, the best results are still far below the estimated human upper-bound, indicating that predicting the distribution of human judgements is still an open, challenging problem with a large room for improvements. We showcase the common errors for MC Dropout and Re-Calibration. Finally, we give guidelines on the usage of these methods with different levels of data availability and encourage future work on modeling the human opinion distribution for language reasoning. Our code and data are publicly available at https://github.com/easonnie/ChaosNLI

We propose a framework for the statistical evaluation of variational auto-encoders (VAEs) and test two instances of this framework in the context of modelling images of handwritten digits and a corpus of English text. Our take on evaluation is based on the idea of statistical model criticism, popular in Bayesian data analysis, whereby a statistical model is evaluated in terms of its ability to reproduce statistics of an unknown data generating process from which we can obtain samples. A VAE learns not one, but two joint distributions over a shared sample space, each exploiting a choice of factorisation that makes sampling tractable in one of two directions (latent-to-data, data-to-latent). We evaluate samples from these distributions, assessing their (marginal) fit to the observed data and our choice of prior, and we also evaluate samples through a pipeline that connects the two distributions starting from a data sample, assessing whether together they exploit and reveal latent factors of variation that are useful to a practitioner. We show that this methodology offers possibilities for model selection qualitatively beyond intrinsic evaluation metrics and at a finer granularity than commonly used statistics can offer.

As the demand for mobility in our society seems to increase, the various issues centered on urban mobility are among those that worry most city inhabitants in this planet. For instance, how to go from A to B in an efficient (but also less stressful) way? These questions and concerns have not changed even during the covid-19 pandemic; on the contrary, as the current stand, people who are avoiding public transportation are only contributing to an increase in the vehicular traffic. The are of intelligent transportation systems (ITS) aims at investigating how to employ information and communication technologies to problems related to transportation. This may mean monitoring and managing the infrastructure (e.g., traffic roads, traffic signals, etc.). However, currently, ITS is also targeting the management of demand. In this panorama, artificial intelligence plays an important role, especially with the advances in machine learning that translates in the use of computational vision, connected and autonomous vehicles, agent-based simulation, among others. In the present work, a survey of several works developed by our group are discussed in a holistic perspective, i.e., they cover not only the supply side (as commonly found in ITS works), but also the demand side, and, in an novel perspective, the integration of both.

High-dimensional spatio-temporal dynamics can often be encoded in a low-dimensional subspace. Engineering applications for modeling, characterization, design, and control of such large-scale systems often rely on dimensionality reduction to make solutions computationally tractable in real-time. Common existing paradigms for dimensionality reduction include linear methods, such as the singular value decomposition (SVD), and nonlinear methods, such as variants of convolutional autoencoders (CAE). However, these encoding techniques lack the ability to efficiently represent the complexity associated with spatio-temporal data, which often requires variable geometry, non-uniform grid resolution, adaptive meshing, and/or parametric dependencies.To resolve these practical engineering challenges, we propose a general framework called Neural Implicit Flow (NIF) that enables a mesh-agnostic, low-rank representation of large-scale, parametric, spatial-temporal data. NIF consists of two modified multilayer perceptrons (MLPs): (i) ShapeNet, which isolates and represents the spatial complexity, and (ii) ParameterNet, which accounts for any other input complexity, including parametric dependencies, time, and sensor measurements. We demonstrate the utility of NIF for parametric surrogate modeling, enabling the interpretable representation and compression of complex spatio-temporal dynamics, efficient many-spatial-query tasks, and improved generalization performance for sparse reconstruction.

ImageNet serves as the primary dataset for evaluating the quality of computer-vision models. The common practice today is training each architecture with a tailor-made scheme, designed and tuned by an expert. In this paper, we present a unified scheme for training any backbone on ImageNet. The scheme, named USI (Unified Scheme for ImageNet), is based on knowledge distillation and modern tricks. It requires no adjustments or hyper-parameters tuning between different models, and is efficient in terms of training times. We test USI on a wide variety of architectures, including CNNs, Transformers, Mobile-oriented and MLP-only. On all models tested, USI outperforms previous state-of-the-art results. Hence, we are able to transform training on ImageNet from an expert-oriented task to an automatic seamless routine. Since USI accepts any backbone and trains it to top results, it also enables to perform methodical comparisons, and identify the most efficient backbones along the speed-accuracy Pareto curve. Implementation is available at:https://github.com/Alibaba-MIIL/Solving_ImageNet

We investigate the "Visual Pushing for Grasping" (VPG) system by Zeng et al. and the "Hourglass" system by Ewerton et al., an evolution of the former. The focus of our work is the investigation of the capabilities of both systems to learn long-term rewards and policies. Zeng et al. original task only needs a limited amount of foresight. Ewerton et al. attain their best performance using an agent which only takes the most immediate action under consideration. We are interested in the ability of their models and training algorithms to accurately predict long-term Q-Values. To evaluate this ability, we design a new bin sorting task and reward function. Our task requires agents to accurately estimate future rewards and therefore use high discount factors in their Q-Value calculation. We investigate the behaviour of an adaptation of the VPG training algorithm on our task. We show that this adaptation can not accurately predict the required long-term action sequences. In addition to the limitations identified by Ewerton et al., it suffers from the known Deep Q-Learning problem of overestimated Q-Values. In an effort to solve our task, we turn to the Hourglass models and combine them with the Double Q-Learning approach. We show that this approach enables the models to accurately predict long-term action sequences when trained with large discount factors. Our results show that the Double Q-Learning technique is essential for training with very high discount factors, as the models Q-Value predictions diverge otherwise. We also experiment with different approaches for discount factor scheduling, loss calculation and exploration procedures. Our results show that the latter factors do not visibly influence the model's performance for our task.

We introduce compositional soft prompting (CSP), a parameter-efficient learning technique to improve the zero-shot compositionality of large-scale pretrained vision-language models (VLMs) without the overhead of fine-tuning the entire model. VLMs can represent arbitrary classes as natural language prompts in their flexible text encoders but they underperform state-of-the-art methods on compositional zero-shot benchmark tasks. To improve VLMs, we propose a novel form of soft prompting. We treat the attributes and objects that are composed to define classes as learnable tokens of vocabulary and tune them on multiple prompt compositions. During inference, we recompose the learned attribute-object vocabulary in new combinations and show that CSP outperforms the original VLM on benchmark datasets by an average of 14.7 percentage points of accuracy. CSP also achieves new state-of-the-art accuracies on two out of three benchmark datasets, while only fine-tuning a small number of parameters. Further, we show that CSP improves generalization to higher-order attribute-attribute-object compositions and combinations of pretrained attributes and fine-tuned objects.

Access to data and data processing, including the use of machine learning techniques, has become significantly easier and cheaper in recent years. Nevertheless, solutions that can be widely adopted by regulators for market monitoring and inspection targeting in a data-driven way have not been frequently discussed by the scientific community. This article discusses the need and the difficulties for the development of such solutions, presents an effective method to address regulation planning, and illustrates its use to account for the most important and common subject for the majority of regulators: the consumer. This article hopes to contribute to increase the awareness of the regulatory community to the need for data processing methods that are objective, impartial, transparent, explainable, simple to implement and with low computational cost, aiming to the implementation of risk-based regulation in the world.

The appearance of complex attention-based language models such as BERT, Roberta or GPT-3 has allowed to address highly complex tasks in a plethora of scenarios. However, when applied to specific domains, these models encounter considerable difficulties. This is the case of Social Networks such as Twitter, an ever-changing stream of information written with informal and complex language, where each message requires careful evaluation to be understood even by humans given the important role that context plays. Addressing tasks in this domain through Natural Language Processing involves severe challenges. When powerful state-of-the-art multilingual language models are applied to this scenario, language specific nuances use to get lost in translation. To face these challenges we present \textbf{BERTuit}, the larger transformer proposed so far for Spanish language, pre-trained on a massive dataset of 230M Spanish tweets using RoBERTa optimization. Our motivation is to provide a powerful resource to better understand Spanish Twitter and to be used on applications focused on this social network, with special emphasis on solutions devoted to tackle the spreading of misinformation in this platform. BERTuit is evaluated on several tasks and compared against M-BERT, XLM-RoBERTa and XLM-T, very competitive multilingual transformers. The utility of our approach is shown with applications, in this case: a zero-shot methodology to visualize groups of hoaxes and profiling authors spreading disinformation. Misinformation spreads wildly on platforms such as Twitter in languages other than English, meaning performance of transformers may suffer when transferred outside English speaking communities.

Various neural network models have been proposed to tackle combinatorial optimization problems such as the travelling salesman problem (TSP). Existing learning-based TSP methods adopt a simple setting that the training and testing data are independent and identically distributed. However, the existing literature fails to solve TSP instances when training and testing data have different distributions. Concretely, we find that different training and testing distribution will result in more difficult TSP instances, i.e., the solution obtained by the model has a large gap from the optimal solution. To tackle this problem, in this work, we study learning-based TSP methods when training and testing data have different distributions using adaptive-hardness, i.e., how difficult a TSP instance can be for a solver. This problem is challenging because it is non-trivial to (1) define hardness measurement quantitatively; (2) efficiently and continuously generate sufficiently hard TSP instances upon model training; (3) fully utilize instances with different levels of hardness to learn a more powerful TSP solver. To solve these challenges, we first propose a principled hardness measurement to quantify the hardness of TSP instances. Then, we propose a hardness-adaptive generator to generate instances with different hardness. We further propose a curriculum learner fully utilizing these instances to train the TSP solver. Experiments show that our hardness-adaptive generator can generate instances ten times harder than the existing methods, and our proposed method achieves significant improvement over state-of-the-art models in terms of the optimality gap.

We investigate and leverage a connection between Differential Privacy (DP) and the recently proposed notion of Distributional Generalization (DG). Applying this connection, we introduce new conceptual tools for designing deep-learning methods that bypass "pathologies" of standard stochastic gradient descent (SGD). First, we prove that differentially private methods satisfy a "What You See Is What You Get (WYSIWYG)" generalization guarantee: whatever a model does on its train data is almost exactly what it will do at test time. This guarantee is formally captured by distributional generalization. WYSIWYG enables principled algorithm design in deep learning by reducing $\textit{generalization}$ concerns to $\textit{optimization}$ ones: in order to mitigate unwanted behavior at test time, it is provably sufficient to mitigate this behavior on the train data. This is notably false for standard (non-DP) methods, hence this observation has applications even when privacy is not required. For example, importance sampling is known to fail for standard SGD, but we show that it has exactly the intended effect for DP-trained models. Thus, with DP-SGD, unlike with SGD, we can influence test-time behavior by making principled train-time interventions. We use these insights to construct simple algorithms which match or outperform SOTA in several distributional robustness applications, and to significantly improve the privacy vs. disparate impact trade-off of DP-SGD. Finally, we also improve on known theoretical bounds relating differential privacy, stability, and distributional generalization.

Reduced-order modelling and low-dimensional surrogate models generated using machine learning algorithms have been widely applied in high-dimensional dynamical systems to improve the algorithmic efficiency. In this paper, we develop a system which combines reduced-order surrogate models with a novel data assimilation (DA) technique used to incorporate real-time observations from different physical spaces. We make use of local smooth surrogate functions which link the space of encoded system variables and the one of current observations to perform variational DA with a low computational cost. The new system, named Generalised Latent Assimilation can benefit both the efficiency provided by the reduced-order modelling and the accuracy of data assimilation. A theoretical analysis of the difference between surrogate and original assimilation cost function is also provided in this paper where an upper bound, depending on the size of the local training set, is given. The new approach is tested on a high-dimensional CFD application of a two-phase liquid flow with non-linear observation operators that current Latent Assimilation methods can not handle. Numerical results demonstrate that the proposed assimilation approach can significantly improve the reconstruction and prediction accuracy of the deep learning surrogate model which is nearly 1000 times faster than the CFD simulation.

Graph neural networks are powerful methods to handle graph-structured data. However, existing graph neural networks only learn higher-order feature interactions implicitly. Thus, they cannot capture information that occurred in low-order feature interactions. To overcome this problem, we propose Explicit Feature Interaction-aware Graph Neural Network (EFI-GNN), which explicitly learns arbitrary-order feature interactions. EFI-GNN can jointly learn with any other graph neural network. We demonstrate that the joint learning method always enhances performance on the various node classification tasks. Furthermore, since EFI-GNN is inherently a linear model, we can interpret the prediction result of EFI-GNN. With the computation rule, we can obtain an any-order feature's effect on the decision. By that, we visualize the effects of the first-order and second-order features as a form of a heatmap.

High-contrast imaging of exoplanets hinges on powerful post-processing methods to denoise the data and separate the signal of a companion from its host star, which is typically orders of magnitude brighter. Existing post-processing algorithms do not use all prior domain knowledge that is available about the problem. We propose a new method that builds on our understanding of the systematic noise and the causal structure of the data-generating process. Our algorithm is based on a modified version of half-sibling regression (HSR), a flexible denoising framework that combines ideas from the fields of machine learning and causality. We adapt the method to address the specific requirements of high-contrast exoplanet imaging data obtained in pupil tracking mode. The key idea is to estimate the systematic noise in a pixel by regressing the time series of this pixel onto a set of causally independent, signal-free predictor pixels. We use regularized linear models in this work; however, other (non-linear) models are also possible. In a second step, we demonstrate how the HSR framework allows us to incorporate observing conditions such as wind speed or air temperature as additional predictors. When we apply our method to four data sets from the VLT/NACO instrument, our algorithm provides a better false-positive fraction than PCA-based PSF subtraction, a popular baseline method in the field. Additionally, we find that the HSR-based method provides direct and accurate estimates for the contrast of the exoplanets without the need to insert artificial companions for calibration in the data sets. Finally, we present first evidence that using the observing conditions as additional predictors can improve the results. Our HSR-based method provides an alternative, flexible and promising approach to the challenge of modeling and subtracting the stellar PSF and systematic noise in exoplanet imaging data.

Modeling effective representations using multiple views that positively influence each other is challenging, and the existing methods perform poorly on Electroencephalogram (EEG) signals for sleep-staging tasks. In this paper, we propose a novel multi-view self-supervised method (mulEEG) for unsupervised EEG representation learning. Our method attempts to effectively utilize the complementary information available in multiple views to learn better representations. We introduce diverse loss that further encourages complementary information across multiple views. Our method with no access to labels beats the supervised training while outperforming multi-view baseline methods on transfer learning experiments carried out on sleep-staging tasks. We posit that our method was able to learn better representations by using complementary multi-views.

We present Perceive-Represent-Generate (PRG), a novel three-stage framework that maps perceptual information of different modalities (e.g., visual or sound), corresponding to a sequence of instructions, to an adequate sequence of movements to be executed by a robot. In the first stage, we perceive and pre-process the given inputs, isolating individual commands from the complete instruction provided by a human user. In the second stage we encode the individual commands into a multimodal latent space, employing a deep generative model. Finally, in the third stage we convert the multimodal latent values into individual trajectories and combine them into a single dynamic movement primitive, allowing its execution in a robotic platform. We evaluate our pipeline in the context of a novel robotic handwriting task, where the robot receives as input a word through different perceptual modalities (e.g., image, sound), and generates the corresponding motion trajectory to write it, creating coherent and readable handwritten words.

Research in manipulation of deformable objects is typically conducted on a limited range of scenarios, because handling each scenario on hardware takes significant effort. Realistic simulators with support for various types of deformations and interactions have the potential to speed up experimentation with novel tasks and algorithms. However, for highly deformable objects it is challenging to align the output of a simulator with the behavior of real objects. Manual tuning is not intuitive, hence automated methods are needed. We view this alignment problem as a joint perception-inference challenge and demonstrate how to use recent neural network architectures to successfully perform simulation parameter inference from real point clouds. We analyze the performance of various architectures, comparing their data and training requirements. Furthermore, we propose to leverage differentiable point cloud sampling and differentiable simulation to significantly reduce the time to achieve the alignment. We employ an efficient way to propagate gradients from point clouds to simulated meshes and further through to the physical simulation parameters, such as mass and stiffness. Experiments with highly deformable objects show that our method can achieve comparable or better alignment with real object behavior, while reducing the time needed to achieve this by more than an order of magnitude. Videos and supplementary material are available at https://tinyurl.com/diffcloud.

Despite its technological benefits, Internet of Things (IoT) has cyber weaknesses due to the vulnerabilities in the wireless medium. Machine learning (ML)-based methods are widely used against cyber threats in IoT networks with promising performance. Advanced persistent threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics. However, it is difficult to apply ML-based approaches to identify APT attacks to obtain a promising detection performance due to an extremely small percentage among normal traffic. There are limited surveys to fully investigate APT attacks in IoT networks due to the lack of public datasets with all types of APT attacks. It is worth to bridge the state-of-the-art in network attack detection with APT attack detection in a comprehensive review article. This survey article reviews the security challenges in IoT networks and presents the well-known attacks, APT attacks, and threat models in IoT systems. Meanwhile, signature-based, anomaly-based, and hybrid intrusion detection systems are summarized for IoT networks. The article highlights statistical insights regarding frequently applied ML-based methods against network intrusion alongside the number of attacks types detected. Finally, open issues and challenges for common network intrusion and APT attacks are presented for future research.

Learning complex time series forecasting models usually requires a large amount of data, as each model is trained from scratch for each task/data set. Leveraging learning experience with similar datasets is a well-established technique for classification problems called few-shot classification. However, existing approaches cannot be applied to time-series forecasting because i) multivariate time-series datasets have different channels and ii) forecasting is principally different from classification. In this paper we formalize the problem of few-shot forecasting of time-series with heterogeneous channels for the first time. Extending recent work on heterogeneous attributes in vector data, we develop a model composed of permutation-invariant deep set-blocks which incorporate a temporal embedding. We assemble the first meta-dataset of 40 multivariate time-series datasets and show through experiments that our model provides a good generalization, outperforming baselines carried over from simpler scenarios that either fail to learn across tasks or miss temporal information.

The performance of a machine learning model degrades when it is applied to data from a similar but different domain than the data it has initially been trained on. To mitigate this domain shift problem, domain adaptation (DA) techniques search for an optimal transformation that converts the (current) input data from a source domain to a target domain to learn a domain-invariant representations that reduces domain discrepancy. This paper proposes a novel supervised domain adaptation based on two steps. First, we search for an optimal class-dependent transformation from the source to the target domain from a few samples. We consider optimal transport methods such as the earth mover distance with Laplacian regularization, Sinkhorn transport and correlation alignment. Second, we use embedding similarity techniques to select the corresponding transformation at inference. We use correlation metrics and maximum mean discrepancy with higher-order moment matching techniques. We conduct an extensive evaluation on time-series datasets with domain shift including simulated and various online handwriting datasets to demonstrate the performance.

Energy-efficient machine learning models that can run directly on edge devices are of great interest in IoT applications, as they can reduce network pressure and response latency, and improve privacy. An effective way to obtain energy-efficiency with small accuracy drops is to sequentially execute a set of increasingly complex models, early-stopping the procedure for "easy" inputs that can be confidently classified by the smallest models. As a stopping criterion, current methods employ a single threshold on the output probabilities produced by each model. In this work, we show that such a criterion is sub-optimal for datasets that include classes of different complexity, and we demonstrate a more general approach based on per-classes thresholds. With experiments on a low-power end-node, we show that our method can significantly reduce the energy consumption compared to the single-threshold approach.

Currently, pre-trained models can be considered the default choice for a wide range of NLP tasks. Despite their SoTA results, there is practical evidence that these models may require a different number of computing layers for different input sequences, since evaluating all layers leads to overconfidence on wrong predictions (namely overthinking). This problem can potentially be solved by implementing adaptive computation time approaches, which were first designed to improve inference speed. Recently proposed PonderNet may be a promising solution for performing an early exit by treating the exit layers index as a latent variable. However, the originally proposed exit criterion, relying on sampling from trained posterior distribution on the probability of exiting from i-th layer, introduces major variance in model outputs, significantly reducing the resulting models performance. In this paper, we propose Ponder ALBERT (PALBERT): an improvement to PonderNet with a novel deterministic Q-exit criterion and a revisited model architecture. We compared PALBERT with recent methods for performing an early exit. We observed that the proposed changes can be considered significant improvements on the original PonderNet architecture and outperform PABEE on a wide range of GLUE tasks. In addition, we also performed an in-depth ablation study of the proposed architecture to further understand Lambda layers and their performance.

We present Continual Inference, a Python library for implementing Continual Inference Networks (CINs) in PyTorch, a class of Neural Networks designed specifically for efficient inference in both online and batch processing scenarios. We offer a comprehensive introduction and guide to CINs and their implementation in practice, and provide best-practices and code examples for composing complex modules for modern Deep Learning. Continual Inference is readily downloadable via the Python Package Index and at \url{www.github.com/lukashedegaard/continual-inference}.

Nowadays, deep neural networks are being used in many domains because of their high accuracy results. However, they are considered as "black box", means that they are not explainable for humans. On the other hand, in some tasks such as medical, economic, and self-driving cars, users want the model to be interpretable to decide if they can trust these results or not. In this work, we present a modified version of an autoencoder-based approach for local interpretability called ALIME. The ALIME itself is inspired by a famous method called Local Interpretable Model-agnostic Explanations (LIME). LIME generates a single instance level explanation by generating new data around the instance and training a local linear interpretable model. ALIME uses an autoencoder to weigh the new data around the sample. Nevertheless, the ALIME uses a linear model as the interpretable model to be trained locally, just like the LIME. This work proposes a new approach, which uses a decision tree instead of the linear model, as the interpretable model. We evaluate the proposed model in case of stability, local fidelity, and interpretability on different datasets. Compared to ALIME, the experiments show significant results on stability and local fidelity and improved results on interpretability.

Pretrained models are the standard starting point for training. This approach consistently outperforms the use of a random initialization. However, pretraining is a costly endeavour that few can undertake. In this paper, we create better base models at hardly any cost, by fusing multiple existing fine tuned models into one. Specifically, we fuse by averaging the weights of these models. We show that the fused model results surpass the pretrained model ones. We also show that fusing is often better than intertraining. We find that fusing is less dependent on the target task. Furthermore, weight decay nullifies intertraining effects but not those of fusing.

We present a new framework AMOS that pretrains text encoders with an Adversarial learning curriculum via a Mixture Of Signals from multiple auxiliary generators. Following ELECTRA-style pretraining, the main encoder is trained as a discriminator to detect replaced tokens generated by auxiliary masked language models (MLMs). Different from ELECTRA which trains one MLM as the generator, we jointly train multiple MLMs of different sizes to provide training signals at various levels of difficulty. To push the discriminator to learn better with challenging replaced tokens, we learn mixture weights over the auxiliary MLMs' outputs to maximize the discriminator loss by backpropagating the gradient from the discriminator via Gumbel-Softmax. For better pretraining efficiency, we propose a way to assemble multiple MLMs into one unified auxiliary model. AMOS outperforms ELECTRA and recent state-of-the-art pretrained models by about 1 point on the GLUE benchmark for BERT base-sized models.

Topic models are some of the most popular ways to represent textual data in an interpret-able manner. Recently, advances in deep generative models, specifically auto-encoding variational Bayes (AEVB), have led to the introduction of unsupervised neural topic models, which leverage deep generative models as opposed to traditional statistics-based topic models. We extend upon these neural topic models by introducing the Label-Indexed Neural Topic Model (LI-NTM), which is, to the extent of our knowledge, the first effective upstream semi-supervised neural topic model. We find that LI-NTM outperforms existing neural topic models in document reconstruction benchmarks, with the most notable results in low labeled data regimes and for data-sets with informative labels; furthermore, our jointly learned classifier outperforms baseline classifiers in ablation studies.

Recent years have witnessed a growing interest in the topic of min-max optimization, owing to its relevance in the context of generative adversarial networks (GANs), robust control and optimization, and reinforcement learning. Motivated by this line of work, we consider a multi-agent min-max learning problem, and focus on the emerging challenge of contending with worst-case Byzantine adversarial agents in such a setup. By drawing on recent results from robust statistics, we design a robust distributed variant of the extra-gradient algorithm - a popular algorithmic approach for min-max optimization. Our main contribution is to provide a crisp analysis of the proposed robust extra-gradient algorithm for smooth convex-concave and smooth strongly convex-strongly concave functions. Specifically, we establish statistical rates of convergence to approximate saddle points. Our rates are near-optimal, and reveal both the effect of adversarial corruption and the benefit of collaboration among the non-faulty agents. Notably, this is the first paper to provide formal theoretical guarantees for large-scale distributed min-max learning in the presence of adversarial agents.

The large transformer-based language models demonstrate excellent performance in natural language processing. By considering the closeness of natural languages to the high-level programming language such as C/C++, this work studies how good are the large transformer-based language models detecting software vulnerabilities. Our results demonstrate the well performance of these models on software vulnerability detection. The answer enables extending transformer-based language models to vulnerability detection and leveraging superior performance beyond the natural language processing domain. Besides, we perform the model's security check using Microsoft's Counterfit, a command-line tool to assess the model's security. Our results find that these models are vulnerable to adversarial examples. In this regard, we present a simple countermeasure and its result. Experimenting with large models is always a challenge due to the requirement of computing resources and platforms/libraries & dependencies. Based on the experiences and difficulties we faced during this work, we present our recommendation while choosing the platforms to run these large models. Moreover, the popular platforms are surveyed thoroughly in this paper.

Hybrid closed loop systems represent the future of care for people with type 1 diabetes (T1D). These devices usually utilise simple control algorithms to select the optimal insulin dose for maintaining blood glucose levels within a healthy range. Online reinforcement learning (RL) has been utilised as a method for further enhancing glucose control in these devices. Previous approaches have been shown to reduce patient risk and improve time spent in the target range when compared to classical control algorithms, but are prone to instability in the learning process, often resulting in the selection of unsafe actions. This work presents an evaluation of offline RL as a means for developing clinically effective dosing policies without the need for patient interaction. This paper examines the utility of BCQ, CQL and TD3-BC in managing the blood glucose of nine virtual patients within the UVA/Padova glucose dynamics simulator. When trained on less than a tenth of the data required by online RL approaches, this work shows that offline RL can significantly increase time in the healthy blood glucose range when compared to the strongest state-of-art baseline. This is achieved without any associated increase in low blood glucose events. Offline RL is also shown to be able to correct for common and challenging scenarios such as incorrect bolus dosing, irregular meal timings and sub-optimal training data.

Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. Based on this, the young research field of Graph Neural Networks (GNNs) has emerged. Despite the youth of the field and the speed in which new models are developed, many good surveys have been published in the last years. Nevertheless, an overview on which graph types can be modeled by GNNs is missing. In this survey, we give a detailed overview of already existing GNNs and, unlike previous surveys, categorize them according to their ability to handle different graph types. We consider GNNs operating on static as well as on dynamic graphs of different structural constitutions, with or without node or edge attributes. Moreover in the dynamic case, we separate the models in discrete-time and continuous-time dynamic graphs based on their architecture. According to our findings, there are still graph types, that are not covered by existing GNN models. Specifically, models concerning heterogeneity in attributes are missing and the deletion of nodes and edges is only covered rarely.

Unsupervised Source (data) Free domain adaptation (USFDA) aims to transfer knowledge from a well-trained source model to a related but unlabeled target domain. In such a scenario, all conventional adaptation methods that require source data fail. To combat this challenge, existing USFDAs turn to transfer knowledge by aligning the target feature to the latent distribution hidden in the source model. However, such information is naturally limited. Thus, the alignment in such a scenario is not only difficult but also insufficient, which degrades the target generalization performance. To relieve this dilemma in current USFDAs, we are motivated to explore a new perspective to boost their performance. For this purpose and gaining necessary insight, we look back upon the origin of the domain adaptation and first theoretically derive a new-brand target generalization error bound based on the model smoothness. Then, following the theoretical insight, a general and model-smoothness-guided Jacobian norm (JN) regularizer is designed and imposed on the target domain to mitigate this dilemma. Extensive experiments are conducted to validate its effectiveness. In its implementation, just with a few lines of codes added to the existing USFDAs, we achieve superior results on various benchmark datasets.

The widespread application of artificial intelligence in health research is currently hampered by limitations in data availability. Distributed learning methods such as federated learning (FL) and shared learning (SL) are introduced to solve this problem as well as data management and ownership issues with their different strengths and weaknesses. The recent proposal of federated split task-agnostic (FeSTA) learning tries to reconcile the distinct merits of FL and SL by enabling the multi-task collaboration between participants through Vision Transformer (ViT) architecture, but they suffer from higher communication overhead. To address this, here we present a multi-task distributed learning using ViT with random patch permutation. Instead of using a CNN based head as in FeSTA, p-FeSTA adopts a randomly permuting simple patch embedder, improving the multi-task learning performance without sacrificing privacy. Experimental results confirm that the proposed method significantly enhances the benefit of multi-task collaboration, communication efficiency, and privacy preservation, shedding light on practical multi-task distributed learning in the field of medical imaging.

We target the problem of automatically synthesizing proofs of semantic equivalence between two programs made of sequences of statements. We represent programs using abstract syntax trees (AST), where a given set of semantics-preserving rewrite rules can be applied on a specific AST pattern to generate a transformed and semantically equivalent program. In our system, two programs are equivalent if there exists a sequence of application of these rewrite rules that leads to rewriting one program into the other. We propose a neural network architecture based on a transformer model to generate proofs of equivalence between program pairs. The system outputs a sequence of rewrites, and the validity of the sequence is simply checked by verifying it can be applied. If no valid sequence is produced by the neural network, the system reports the programs as non-equivalent, ensuring by design no programs may be incorrectly reported as equivalent. Our system is fully implemented for a given grammar. To efficiently train the system to generate such sequences, we develop an original incremental training technique, named self-supervised sample selection. We extensively study the effectiveness of this novel training approach on proofs of increasing complexity and length. Our system, S4Eq, achieves 97% proof success on a curated dataset of 10,000 pairs of equivalent programs.

Deep neural networks (DNNs) designed for computer vision and natural language processing tasks cannot be directly applied to the radio frequency (RF) datasets. To address this challenge, we propose to convert the raw RF data to data types that are suitable for off-the-shelf DNNs by introducing a convolutional transform technique. In addition, we propose a simple 5-layer convolutional neural network architecture (CONV-5) that can operate with raw RF I/Q data without any transformation. Further, we put forward an RF dataset, referred to as RF1024, to facilitate future RF research. RF1024 consists of 8 different RF modulation classes with each class having 1000/200 training/test samples. Each sample of the RF1024 dataset contains 1024 complex I/Q values. Lastly, the experiments are performed on the RadioML2016 and RF1024 datasets to demonstrate the improved classification performance.

In this paper, we address the problem of texture representation for 3D shapes for the challenging and underexplored tasks of texture transfer and synthesis. Previous works either apply spherical texture maps which may lead to large distortions, or use continuous texture fields that yield smooth outputs lacking details. We argue that the traditional way of representing textures with images and linking them to a 3D mesh via UV mapping is more desirable, since synthesizing 2D images is a well-studied problem. We propose AUV-Net which learns to embed 3D surfaces into a 2D aligned UV space, by mapping the corresponding semantic parts of different 3D shapes to the same location in the UV space. As a result, textures are aligned across objects, and can thus be easily synthesized by generative models of images. Texture alignment is learned in an unsupervised manner by a simple yet effective texture alignment module, taking inspiration from traditional works on linear subspace learning. The learned UV mapping and aligned texture representations enable a variety of applications including texture transfer, texture synthesis, and textured single view 3D reconstruction. We conduct experiments on multiple datasets to demonstrate the effectiveness of our method. Project page: https://nv-tlabs.github.io/AUV-NET.

Federated Learning (FL) is an emerging framework for distributed processing of large data volumes by edge devices subject to limited communication bandwidths, heterogeneity in data distributions and computational resources, as well as privacy considerations. In this paper, we introduce a new FL protocol termed FedADMM based on primal-dual optimization. The proposed method leverages dual variables to tackle statistical heterogeneity, and accommodates system heterogeneity by tolerating variable amount of work performed by clients. FedADMM maintains identical communication costs per round as FedAvg/Prox, and generalizes them via the augmented Lagrangian. A convergence proof is established for nonconvex objectives, under no restrictions in terms of data dissimilarity or number of participants per round of the algorithm. We demonstrate the merits through extensive experiments on real datasets, under both IID and non-IID data distributions across clients. FedADMM consistently outperforms all baseline methods in terms of communication efficiency, with the number of rounds needed to reach a prescribed accuracy reduced by up to 87%. The algorithm effectively adapts to heterogeneous data distributions through the use of dual variables, without the need for hyperparameter tuning, and its advantages are more pronounced in large-scale systems.

Recently, deep learning (DL)-based non-intrusive speech assessment models have attracted great attention. Many studies report that these DL-based models yield satisfactory assessment performance and good flexibility, but their performance in unseen environments remains a challenge. Furthermore, compared to quality scores, fewer studies elaborate deep learning models to estimate intelligibility scores. This study proposes a multi-task speech intelligibility prediction model, called MTI-Net, for simultaneously predicting human and machine intelligibility measures. Specifically, given a speech utterance, MTI-Net is designed to predict subjective listening test results and word error rate (WER) scores. We also investigate several methods that can improve the prediction performance of MTI-Net. First, we compare different features (including low-level features and embeddings from self-supervised learning (SSL) models) and prediction targets of MTI-Net. Second, we explore the effect of transfer learning and multi-tasking learning on training MTI-Net. Finally, we examine the potential advantages of fine-tuning SSL embeddings. Experimental results demonstrate the effectiveness of using cross-domain features, multi-task learning, and fine-tuning SSL embeddings. Furthermore, it is confirmed that the intelligibility and WER scores predicted by MTI-Net are highly correlated with the ground-truth scores.

Due to the rapid growth of data transmissions in internet of vehicles (IoV), finding schemes that can effectively alleviate access congestion has become an important issue. Recently, many traffic control schemes have been studied. Nevertheless, the dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies, which is significant for the random access resource allocation. In this paper, we consider a hybrid traffic control scheme and use proximal policy optimization (PPO) method to tackle it. Firstly, IoV devices are divided into various classes based on delay characteristics. The target of maximizing the successful transmission of packets with the success rate constraint is established. Then, the optimization objective is transformed into a markov decision process (MDP) model. Finally, the access class barring (ACB) factors are obtained based on the PPO method to maximize the number of successful access devices. The performance of the proposal algorithm in respect of successful events and delay compared to existing schemes is verified by simulations.

Deep Learning-based models have been widely investigated, and they have demonstrated significant performance on non-trivial tasks such as speech recognition, image processing, and natural language understanding. However, this is at the cost of substantial data requirements. Considering the widespread proliferation of edge devices (e.g. Internet of Things devices) over the last decade, Deep Learning in the edge paradigm, such as device-cloud integrated platforms, is required to leverage its superior performance. Moreover, it is suitable from the data requirements perspective in the edge paradigm because the proliferation of edge devices has resulted in an explosion in the volume of generated and collected data. However, there are difficulties due to other requirements such as high computation, high latency, and high bandwidth caused by Deep Learning applications in real-world scenarios. In this regard, this survey paper investigates Deep Learning at the edge, its architecture, enabling technologies, and model adaption techniques, where edge servers and edge devices participate in deep learning training and inference. For simplicity, we call this paradigm the All-in EDGE paradigm. Besides, this paper presents the key performance metrics for Deep Learning at the All-in EDGE paradigm to evaluate various deep learning techniques and choose a suitable design. Moreover, various open challenges arising from the deployment of Deep Learning at the All-in EDGE paradigm are identified and discussed.

We propose a train rescheduling algorithm which applies a standardized feature selection based on pairwise conflicts in order to serve as input for the reinforcement learning framework. We implement an analytical method which identifies and optimally solves every conflict arising between two trains, then we design a corresponding observation space which features the most relevant information considering these conflicts. The data obtained this way then translates to actions in the context of the reinforcement learning framework. We test our preliminary model using the evaluation metrics of the Flatland Challenge. The empirical results indicate that the suggested feature space provides meaningful observations, from which a sensible scheduling policy can be learned.

Adversarial perturbations have drawn great attentions in various deep neural networks. Most of them are computed by iterations and cannot be interpreted very well. In contrast, little attentions are paid to basic machine learning models such as support vector machines. In this paper, we investigate the optimization models and the interpretations for three types of adversarial perturbations against support vector machines, including sample-adversarial perturbations (sAP), class-universal adversarial perturbations (cuAP) as well as universal adversarial perturbations (uAP). For linear binary/multi classification support vector machines (SVMs), we derive the explicit solutions for sAP, cuAP and uAP (binary case), and approximate solution for uAP of multi-classification. We also obtain the upper bound of fooling rate for uAP. Such results not only increase the interpretability of the three adversarial perturbations, but also provide great convenience in computation since iterative process can be avoided. Numerical results show that our method is fast and effective in calculating three types of adversarial perturbations.

Environments in Reinforcement Learning are usually only partially observable. To address this problem, a possible solution is to provide the agent with information about the past. However, providing complete observations of numerous steps can be excessive. Inspired by human memory, we propose to represent history with only important changes in the environment and, in our approach, to obtain automatically this representation using self-supervision. Our method (TempAl) aligns temporally-close frames, revealing a general, slowly varying state of the environment. This procedure is based on contrastive loss, which pulls embeddings of nearby observations to each other while pushing away other samples from the batch. It can be interpreted as a metric that captures the temporal relations of observations. We propose to combine both common instantaneous and our history representation and we evaluate TempAl on all available Atari games from the Arcade Learning Environment. TempAl surpasses the instantaneous-only baseline in 35 environments out of 49. The source code of the method and of all the experiments is available at https://github.com/htdt/tempal.

Direct multi-task twin support vector machine (DMTSVM) explores the shared information between multiple correlated tasks, then it produces better generalization performance. However, it contains matrix inversion operation when solving the dual problems, so it costs much running time. Moreover, kernel trick cannot be directly utilized in the nonlinear case. To effectively avoid above problems, a novel multi-task nonparallel support vector machine (MTNPSVM) including linear and nonlinear cases is proposed in this paper. By introducing epsilon-insensitive loss instead of square loss in DMTSVM, MTNPSVM effectively avoids matrix inversion operation and takes full advantage of the kernel trick. Theoretical implication of the model is further discussed. To further improve the computational efficiency, the alternating direction method of multipliers (ADMM) is employed when solving the dual problem. The computational complexity and convergence of the algorithm are provided. In addition, the property and sensitivity of the parameter in model are further explored. The experimental results on fifteen benchmark datasets and twelve image datasets demonstrate the validity of MTNPSVM in comparison with the state-of-the-art algorithms. Finally, it is applied to real Chinese Wine dataset, and also verifies its effectiveness.

NextG networks are intended to provide the flexibility of sharing the spectrum with incumbent users and support various spectrum monitoring tasks such as anomaly detection, fault diagnostics, user equipment identification, and authentication. A network of wireless sensors is needed to monitor the spectrum for signal transmissions of interest over a large deployment area. Each sensor receives signals under a specific channel condition depending on its location and trains an individual model of a deep neural network (DNN) accordingly to classify signals. To improve the accuracy, individual sensors may exchange sensing data or sensor results with each other or with a fusion center (such as in cooperative spectrum sensing). In this paper, distributed federated learning over a multi-hop wireless network is considered to collectively train a DNN for signal identification. In distributed federated learning, each sensor broadcasts its trained model to its neighbors, collects the DNN models from its neighbors, and aggregates them to initialize its own model for the next round of training. Without exchanging any spectrum data, this process is repeated over time such that a common DNN is built across the network while preserving the privacy associated with signals collected at different locations. Signal classification accuracy and convergence time are evaluated for different network topologies (including line, star, ring, grid, and random networks) and packet loss events. Then, the reduction of communication overhead and energy consumption is considered with random participation of sensors in model updates. The results show the feasibility of extending cooperative spectrum sensing over a general multi-hop wireless network through federated learning and indicate its robustness to wireless network effects, thereby sustaining high accuracy with low communication overhead and energy consumption.

Data-driven algorithm configuration is a promising, learning-based approach for beyond worst-case analysis of algorithms with tunable parameters. An important open problem is the design of efficient data-driven algorithms for algorithm families with more than one parameter. In this work we provide algorithms for efficient (output-polynomial) multidimensional parameter tuning, i.e. for families with a small constant number of parameters, for three very different combinatorial problems -- linkage-based clustering, dynamic programming for sequence alignment, and auction design for two-part tariff schemes. We extend the single-parameter clustering algorithm of Balcan et al. 2020 arXiv:1907.00533 to multiple parameters and to the sequence alignment problem by proposing an execution graph which compactly represents all the states the algorithm could attain for all possible parameter values. A key problem-specific challenge is to efficiently compute how the partition of the parameter space (into regions with unique algorithmic states) changes with a single algorithmic step. We give algorithms which improve on the runtime of previously best known results for linkage-based clustering, sequence alignment and two-part tariff pricing.

Regularization is a fundamental technique to prevent over-fitting and to improve generalization performances by constraining a model's complexity. Current Deep Networks heavily rely on regularizers such as Data-Augmentation (DA) or weight-decay, and employ structural risk minimization, i.e. cross-validation, to select the optimal regularization hyper-parameters. In this study, we demonstrate that techniques such as DA or weight decay produce a model with a reduced complexity that is unfair across classes. The optimal amount of DA or weight decay found from cross-validation leads to disastrous model performances on some classes e.g. on Imagenet with a resnet50, the "barn spider" classification test accuracy falls from $68\%$ to $46\%$ only by introducing random crop DA during training. Even more surprising, such performance drop also appears when introducing uninformative regularization techniques such as weight decay. Those results demonstrate that our search for ever increasing generalization performance -- averaged over all classes and samples -- has left us with models and regularizers that silently sacrifice performances on some classes. This scenario can become dangerous when deploying a model on downstream tasks e.g. an Imagenet pre-trained resnet50 deployed on INaturalist sees its performances fall from $70\%$ to $30\%$ on class \#8889 when introducing random crop DA during the Imagenet pre-training phase. Those results demonstrate that designing novel regularizers without class-dependent bias remains an open research question.

Pre-trained language models (LMs) have been shown to memorize a substantial amount of knowledge from the pre-training corpora; however, they are still limited in recalling factually correct knowledge given a certain context. Hence, they tend to suffer from counterfactual or hallucinatory generation when used in knowledge-intensive natural language generation (NLG) tasks. Recent remedies to this problem focus on modifying either the pre-training or task fine-tuning objectives to incorporate knowledge, which normally require additional costly training or architecture modification of LMs for practical applications. We present Knowledge Infused Decoding (KID) -- a novel decoding algorithm for generative LMs, which dynamically infuses external knowledge into each step of the LM decoding. Specifically, we maintain a local knowledge memory based on the current context, interacting with a dynamically created external knowledge trie, and continuously update the local memory as a knowledge-aware constraint to guide decoding via reinforcement learning. On six diverse knowledge-intensive NLG tasks, task-agnostic LMs (e.g., GPT-2 and BART) armed with KID outperform many task-optimized state-of-the-art models, and show particularly strong performance in few-shot scenarios over seven related knowledge-infusion techniques. Human evaluation confirms KID's ability to generate more relevant and factual language for the input context when compared with multiple baselines. Finally, KID also alleviates exposure bias and provides stable generation quality when generating longer sequences. Code for KID is available at https://github.com/microsoft/KID.

Considering the natural frequency characteristics in sleep medicine, this paper first proposes a time-frequency framework for the representation learning of the electroencephalogram (EEG) following the definition of the American Academy of Sleep Medicine. To meet the temporal-random and transient nature of the defining characteristics of sleep stages, we further design a context-sensitive flexible pipeline that automatically adapts to the attributes of data itself. That is, the input EEG spectrogram is partitioned into a sequence of patches in the time and frequency axes, and then input to a delicate deep learning network for further representation learning to extract the stage-dependent features, which are used in the classification step finally. The proposed pipeline is validated against a large database, i.e., the Sleep Heart Health Study (SHHS), and the results demonstrate that the competitive performance for the wake, N2, and N3 stages outperforms the state-of-art works, with the F1 scores being 0.93, 0.88, and 0.87, respectively, and the proposed method has a high inter-rater reliability of 0.80 kappa. Importantly, we visualize the stage scoring process of the model decision with the Layer-wise Relevance Propagation (LRP) method, which shows that the proposed pipeline is more sensitive and perceivable in the decision-making process than the baseline pipelines. Therefore, the pipeline together with the LRP method can provide better model interpretability, which is important for clinical support.

Generating temporally coherent high fidelity video is an important milestone in generative modeling research. We make progress towards this milestone by proposing a diffusion model for video generation that shows very promising initial results. Our model is a natural extension of the standard image diffusion architecture, and it enables jointly training from image and video data, which we find to reduce the variance of minibatch gradients and speed up optimization. To generate long and higher resolution videos we introduce a new conditional sampling technique for spatial and temporal video extension that performs better than previously proposed methods. We present the first results on a large text-conditioned video generation task, as well as state-of-the-art results on an established unconditional video generation benchmark. Supplementary material is available at https://video-diffusion.github.io/

Multi-view unsupervised feature selection has been proven to be efficient in reducing the dimensionality of multi-view unlabeled data with high dimensions. The previous methods assume all of the views are complete. However, in real applications, the multi-view data are often incomplete, i.e., some views of instances are missing, which will result in the failure of these methods. Besides, while the data arrive in form of streams, these existing methods will suffer the issues of high storage cost and expensive computation time. To address these issues, we propose an Incremental Incomplete Multi-view Unsupervised Feature Selection method (I$^2$MUFS) on incomplete multi-view streaming data. By jointly considering the consistent and complementary information across different views, I$^2$MUFS embeds the unsupervised feature selection into an extended weighted non-negative matrix factorization model, which can learn a consensus clustering indicator matrix and fuse different latent feature matrices with adaptive view weights. Furthermore, we introduce the incremental leaning mechanisms to develop an alternative iterative algorithm, where the feature selection matrix is incrementally updated, rather than recomputing on the entire updated data from scratch. A series of experiments are conducted to verify the effectiveness of the proposed method by comparing with several state-of-the-art methods. The experimental results demonstrate the effectiveness and efficiency of the proposed method in terms of the clustering metrics and the computational cost.

Code search aims to retrieve the most semantically relevant code snippet for a given natural language query. Recently, large-scale code pre-trained models such as CodeBERT and GraphCodeBERT learn generic representations of source code and have achieved substantial improvement on code search task. However, the high-quality sequence-level representations of code snippets have not been sufficiently explored. In this paper, we propose a new approach with multimodal contrastive learning and soft data augmentation for code search. Multimodal contrastive learning is used to pull together the representations of code-query pairs and push apart the unpaired code snippets and queries. Moreover, data augmentation is critical in contrastive learning for learning high-quality representations. However, only semantic-preserving augmentations for source code are considered in existing work. In this work, we propose to do soft data augmentation by dynamically masking and replacing some tokens in code sequences to generate code snippets that are similar but not necessarily semantic-preserving as positive samples for paired queries. We conduct extensive experiments to evaluate the effectiveness of our approach on a large-scale dataset with six programming languages. The experimental results show that our approach significantly outperforms the state-of-the-art methods. We also adapt our techniques to several pre-trained models such as RoBERTa and CodeBERT, and significantly boost their performance on the code search task.

Voice cloning is a difficult task which requires robust and informative features incorporated in a high quality TTS system in order to effectively copy an unseen speaker's voice. In our work, we utilize features learned in a self-supervised framework via the Bootstrap Your Own Latent (BYOL) method, which is shown to produce high quality speech representations when specific audio augmentations are applied to the vanilla algorithm. We further extend the augmentations in the training procedure to aid the resulting features to capture the speaker identity and to make them robust to noise and acoustic conditions. The learned features are used as pre-trained utterance-level embeddings and as inputs to a Non-Attentive Tacotron based architecture, aiming to achieve multispeaker speech synthesis without utilizing additional speaker features. This method enables us to train our model in an unlabeled multispeaker dataset as well as use unseen speaker embeddings to copy a speaker's voice. Subjective and objective evaluations are used to validate the proposed model, as well as the robustness to the acoustic conditions of the target utterance.

Recently there has seen promising results on automatic stage scoring by extracting spatio-temporal features from electroencephalogram (EEG). Such methods entail laborious manual feature engineering and domain knowledge. In this study, we propose an adaptive scheme to probabilistically encode, filter and accumulate the input signals and weight the resultant features by the half-Gaussian probabilities of signal intensities. The adaptive representations are subsequently fed into a transformer model to automatically mine the relevance between features and corresponding stages. Extensive experiments on the largest public dataset against state-of-the-art methods validate the effectiveness of our proposed method and reveal promising future directions.

Recurrent neural networks (RNNs) have many advantages over more traditional system identification techniques. They may be applied to linear and nonlinear systems, and they require fewer modeling assumptions. However, these neural network models may also need larger amounts of data to learn and generalize. Furthermore, neural networks training is a time-consuming process. Hence, building upon long-short term memory neural networks (LSTM), this paper proposes using two types of deep transfer learning, namely parameter fine-tuning and freezing, to reduce the data and computation requirements for system identification. We apply these techniques to identify two dynamical systems, namely a second-order linear system and a Wiener-Hammerstein nonlinear system. Results show that compared with direct learning, our method accelerates learning by 10% to 50%, which also saves data and computing resources.

We aim to provably complete a sparse and highly-missing tensor in the presence of covariate information along tensor modes. Our motivation comes from online advertising where users click-through-rates (CTR) on ads over various devices form a CTR tensor that has about 96% missing entries and has many zeros on non-missing entries, which makes the standalone tensor completion method unsatisfactory. Beside the CTR tensor, additional ad features or user characteristics are often available. In this paper, we propose Covariate-assisted Sparse Tensor Completion (COSTCO) to incorporate covariate information for the recovery of the sparse tensor. The key idea is to jointly extract latent components from both the tensor and the covariate matrix to learn a synthetic representation. Theoretically, we derive the error bound for the recovered tensor components and explicitly quantify the improvements on both the reveal probability condition and the tensor recovery accuracy due to covariates. Finally, we apply COSTCO to an advertisement dataset consisting of a CTR tensor and ad covariate matrix, leading to 23% accuracy improvement over the baseline. An important by-product is that ad latent components from COSTCO reveal interesting ad clusters, which are useful for better ad targeting.  ( 2 min )

Principal component analysis (PCA) is a dimensionality reduction method in data analysis that involves diagonalizing the covariance matrix of the dataset. Recently, quantum algorithms have been formulated for PCA based on diagonalizing a density matrix. These algorithms assume that the covariance matrix can be encoded in a density matrix, but a concrete protocol for this encoding has been lacking. Our work aims to address this gap. Assuming amplitude encoding of the data, with the data given by the ensemble $\{p_i,| \psi_i \rangle\}$, then one can easily prepare the ensemble average density matrix $\overline{\rho} = \sum_i p_i |\psi_i\rangle \langle \psi_i |$. We first show that $\overline{\rho}$ is precisely the covariance matrix whenever the dataset is centered. For quantum datasets, we exploit global phase symmetry to argue that there always exists a centered dataset consistent with $\overline{\rho}$, and hence $\overline{\rho}$ can always be interpreted as a covariance matrix. This provides a simple means for preparing the covariance matrix for arbitrary quantum datasets or centered classical datasets. For uncentered classical datasets, our method is so-called "PCA without centering", which we interpret as PCA on a symmetrized dataset. We argue that this closely corresponds to standard PCA, and we derive equations and inequalities that bound the deviation of the spectrum obtained with our method from that of standard PCA. We numerically illustrate our method for the MNIST handwritten digit dataset. We also argue that PCA on quantum datasets is natural and meaningful, and we numerically implement our method for molecular ground-state datasets.  ( 2 min )

In this paper, we develop a distributionally robust chance-constrained formulation of the Optimal Power Flow problem (OPF) whereby the system operator can leverage contextual information. For this purpose, we exploit an ambiguity set based on probability trimmings and optimal transport through which the dispatch solution is protected against the incomplete knowledge of the relationship between the OPF uncertainties and the context that is conveyed by a sample of their joint probability distribution. We provide a tractable reformulation of the proposed distributionally robust chance-constrained OPF problem under the popular conditional-value-at-risk approximation. By way of numerical experiments run on a modified IEEE-118 bus network with wind uncertainty, we show how the power system can substantially benefit from taking into account the well-known statistical dependence between the point forecast of wind power outputs and its associated prediction error. Furthermore, the experiments conducted also reveal that the distributional robustness conferred on the OPF solution by our probability-trimmings-based approach is superior to that bestowed by alternative approaches in terms of expected cost and system reliability.  ( 2 min )

Most real optimization problems are defined over a mixed search space where the variables are both discrete and continuous. In engineering applications, the objective function is typically calculated with a numerically costly black-box simulation.General mixed and costly optimization problems are therefore of a great practical interest, yet their resolution remains in a large part an open scientific question. In this article, costly mixed problems are approached through Gaussian processes where the discrete variables are relaxed into continuous latent variables. The continuous space is more easily harvested by classical Bayesian optimization techniques than a mixed space would. Discrete variables are recovered either subsequently to the continuous optimization, or simultaneously with an additional continuous-discrete compatibility constraint that is handled with augmented Lagrangians. Several possible implementations of such Bayesian mixed optimizers are compared. In particular, the reformulation of the problem with continuous latent variables is put in competition with searches working directly in the mixed space. Among the algorithms involving latent variables and an augmented Lagrangian, a particular attention is devoted to the Lagrange multipliers for which a local and a global estimation techniques are studied. The comparisons are based on the repeated optimization of three analytical functions and a beam design problem.  ( 2 min )

Rapid advancements in deep learning have led to many recent breakthroughs. While deep learning models achieve superior performance, often statistically better than humans, their adoption into safety-critical settings, such as healthcare or self-driving cars is hindered by their inability to provide safety guarantees or to expose the inner workings of the model in a human understandable form. We present Mo\"ET, a novel model based on Mixture of Experts, consisting of decision tree experts and a generalized linear model gating function. Thanks to such gating function the model is more expressive than the standard decision tree. To support non-differentiable decision trees as experts, we formulate a novel training procedure. In addition, we introduce a hard thresholding version, Mo\"ETH, in which predictions are made solely by a single expert chosen via the gating function. Thanks to that property, Mo\"ETH allows each prediction to be easily decomposed into a set of logical rules in a form which can be easily verified. While Mo\"ET is a general use model, we illustrate its power in the reinforcement learning setting. By training Mo\"ET models using an imitation learning procedure on deep RL agents we outperform the previous state-of-the-art technique based on decision trees while preserving the verifiability of the models. Moreover, we show that Mo\"ET can also be used in real-world supervised problems on which it outperforms other verifiable machine learning models.  ( 3 min )

With the surge in the number of hyperparameters and training times of modern machine learning models, hyperparameter tuning is becoming increasingly expensive. However, after assessing 40 tuning methods systematically, we find that each faces certain limitations. In particular, methods that speed up tuning via knowledge transfer typically require the final performance of hyperparameters and do not focus on low-fidelity information. As we demonstrate empirically, this common practice is suboptimal and can incur an unnecessary use of resources. It is more cost-efficient to instead leverage low-fidelity tuning observations to measure inter-task similarity and transfer knowledge from existing to new tasks accordingly. However, performing multi-fidelity tuning comes with its own challenges in the transfer setting: the noise in additional observations and the need for performance forecasting. Therefore, we propose and conduct a thorough analysis of a multi-task multi-fidelity Bayesian optimization framework, which leads to the best instantiation--amortized auto-tuning (AT2). We further present an offline-computed 27-task hyperparameter recommendation (HyperRec) database to serve the community. Extensive experiments on HyperRec and other real-world databases illustrate the effectiveness of our AT2 method.  ( 2 min )

Topic models are some of the most popular ways to represent textual data in an interpret-able manner. Recently, advances in deep generative models, specifically auto-encoding variational Bayes (AEVB), have led to the introduction of unsupervised neural topic models, which leverage deep generative models as opposed to traditional statistics-based topic models. We extend upon these neural topic models by introducing the Label-Indexed Neural Topic Model (LI-NTM), which is, to the extent of our knowledge, the first effective upstream semi-supervised neural topic model. We find that LI-NTM outperforms existing neural topic models in document reconstruction benchmarks, with the most notable results in low labeled data regimes and for data-sets with informative labels; furthermore, our jointly learned classifier outperforms baseline classifiers in ablation studies.  ( 2 min )

We study the basic operation of set union in the global model of differential privacy. In this problem, we are given a universe $U$ of items, possibly of infinite size, and a database $D$ of users. Each user $i$ contributes a subset $W_i \subseteq U$ of items. We want an ($\epsilon$,$\delta$)-differentially private algorithm which outputs a subset $S \subset \cup_i W_i$ such that the size of $S$ is as large as possible. The problem arises in countless real world applications; it is particularly ubiquitous in natural language processing (NLP) applications as vocabulary extraction. For example, discovering words, sentences, $n$-grams etc., from private text data belonging to users is an instance of the set union problem. Known algorithms for this problem proceed by collecting a subset of items from each user, taking the union of such subsets, and disclosing the items whose noisy counts fall above a certain threshold. Crucially, in the above process, the contribution of each individual user is always independent of the items held by other users, resulting in a wasteful aggregation process, where some item counts happen to be way above the threshold. We deviate from the above paradigm by allowing users to contribute their items in a $\textit{dependent fashion}$, guided by a $\textit{policy}$. In this new setting ensuring privacy is significantly delicate. We prove that any policy which has certain $\textit{contractive}$ properties would result in a differentially private algorithm. We design two new algorithms, one using Laplace noise and other Gaussian noise, as specific instances of policies satisfying the contractive properties. Our experiments show that the new algorithms significantly outperform previously known mechanisms for the problem.  ( 3 min )

Machine Learning with Deep Neural Networks (DNNs) has become a successful tool in solving tasks across various fields of application. The success of DNNs is strongly connected to their high complexity in terms of the number of network layers or of neurons in each layer, which severely complicates to understand how DNNs solve their learned task. To improve the explainability of DNNs, we adapt methods from neuroscience because this field has a rich experience in analyzing complex and opaque systems. In this work, we draw inspiration from how neuroscience uses topographic maps to visualize the activity of the brain when it performs certain tasks. Transferring this approach to DNNs can help to visualize and understand their internal processes more intuitively, too. However, the inner structures of brains and DNNs differ substantially. Therefore, to be able to visualize activations of neurons in DNNs as topographic maps, we research techniques to layout the neurons in a two-dimensional space in which neurons of similar activity are in the vicinity of each other. In this work, we introduce and compare different methods to obtain a topographic layout of the neurons in a network layer. Moreover, we demonstrate how to use the resulting topographic activation maps to identify errors or encoded biases in DNNs or data sets. Our novel visualization technique improves the transparency of DNN-based algorithmic decision-making systems and is accessible to a broad audience because topographic maps are intuitive to interpret without expert-knowledge in Machine Learning.

Regularization is a fundamental technique to prevent over-fitting and to improve generalization performances by constraining a model's complexity. Current Deep Networks heavily rely on regularizers such as Data-Augmentation (DA) or weight-decay, and employ structural risk minimization, i.e. cross-validation, to select the optimal regularization hyper-parameters. In this study, we demonstrate that techniques such as DA or weight decay produce a model with a reduced complexity that is unfair across classes. The optimal amount of DA or weight decay found from cross-validation leads to disastrous model performances on some classes e.g. on Imagenet with a resnet50, the "barn spider" classification test accuracy falls from $68\%$ to $46\%$ only by introducing random crop DA during training. Even more surprising, such performance drop also appears when introducing uninformative regularization techniques such as weight decay. Those results demonstrate that our search for ever increasing generalization performance -- averaged over all classes and samples -- has left us with models and regularizers that silently sacrifice performances on some classes. This scenario can become dangerous when deploying a model on downstream tasks e.g. an Imagenet pre-trained resnet50 deployed on INaturalist sees its performances fall from $70\%$ to $30\%$ on class \#8889 when introducing random crop DA during the Imagenet pre-training phase. Those results demonstrate that designing novel regularizers without class-dependent bias remains an open research question.

The estimation of categorical distributions under marginal constraints summarizing some sample from a population in the most-generalizable way is key for many machine-learning and data-driven approaches. We provide a parameter-agnostic theoretical framework that enables this task ensuring (i) that a categorical distribution of Maximum Entropy under marginal constraints always exists and (ii) that it is unique. The procedure of iterative proportional fitting (IPF) naturally estimates that distribution from any consistent set of marginal constraints directly in the space of probabilities, thus deductively identifying a least-biased characterization of the population. The theoretical framework together with IPF leads to a holistic workflow that enables modeling any class of categorical distributions solely using the phenomenological information provided.  ( 2 min )

Generative Flow Networks (GFlowNets) have been introduced as a method to sample a diverse set of candidates in an active learning context, with a training objective that makes them approximately sample in proportion to a given reward function. In this paper, we show a number of additional theoretical properties of GFlowNets. They can be used to estimate joint probability distributions and the corresponding marginal distributions where some variables are unspecified and, of particular interest, can represent distributions over composite objects like sets and graphs. GFlowNets amortize the work typically done by computationally expensive MCMC methods in a single but trained generative pass. They could also be used to estimate partition functions and free energies, conditional probabilities of supersets (supergraphs) given a subset (subgraph), as well as marginal distributions over all supersets (supergraphs) of a given set (graph). We introduce variations enabling the estimation of entropy and mutual information, sampling from a Pareto frontier, connections to reward-maximizing policies, and extensions to stochastic environments, continuous actions and modular energy functions.  ( 2 min )

Streamlined qBOLD acquisitions enable experimentally straightforward observations of brain oxygen metabolism. $R_2^\prime$ maps are easily inferred; however, the Oxygen extraction fraction (OEF) and deoxygenated blood volume (DBV) are more ambiguously determined from the data. As such, existing inference methods tend to yield very noisy and underestimated OEF maps, while overestimating DBV. This work describes a novel probabilistic machine learning approach that can infer plausible distributions of OEF and DBV. Initially, we create a model that produces informative voxelwise prior distribution based on synthetic training data. Contrary to prior work, we model the joint distribution of OEF and DBV through a scaled multivariate logit-Normal distribution, which enables the values to be constrained within a plausible range. The prior distribution model is used to train an efficient amortized variational Bayesian inference model. This model learns to infer OEF and DBV by predicting real image data, with few training data required, using the signal equations as a forward model. We demonstrate that our approach enables the inference of smooth OEF and DBV maps, with a physiologically plausible distribution that can be adapted through specification of an informative prior distribution. Other benefits include model comparison (via the evidence lower bound) and uncertainty quantification for identifying image artefacts. Results are demonstrated on a small study comparing subjects undergoing hyperventilation and at rest. We illustrate that the proposed approach allows measurement of gray matter differences in OEF and DBV and enables voxelwise comparison between conditions, where we observe significant increases in OEF and $R_2^\prime$ during hyperventilation.  ( 2 min )

Clustering algorithms partition a dataset into groups of similar points. The primary contribution of this article is the Multiscale Spatially-Regularized Diffusion Learning (M-SRDL) clustering algorithm, which uses spatially-regularized diffusion distances to efficiently and accurately learn multiple scales of latent structure in hyperspectral images. The M-SRDL clustering algorithm extracts clusterings at many scales from a hyperspectral image and outputs these clusterings' variation of information-barycenter as an exemplar for all underlying cluster structure. We show that incorporating spatial regularization into a multiscale clustering framework results in smoother and more coherent clusters when applied to hyperspectral data, yielding more accurate clustering labels.  ( 2 min )

Gradient-enhanced Kriging (GE-Kriging) is a well-established surrogate modelling technique for approximating expensive computational models. However, it tends to get impractical for high-dimensional problems due to the large inherent correlation matrix and the associated high-dimensional hyper-parameter tuning problem. To address these issues, we propose a new method in this paper, called sliced GE-Kriging (SGE-Kriging) for reducing both the size of the correlation matrix and the number of hyper-parameters. Firstly, we perform a derivative-based global sensitivity analysis to detect the relative importance of each input variable with respect to model response. Then, we propose to split the training sample set into multiple slices, and invoke Bayes' theorem to approximate the full likelihood function via a sliced likelihood function, in which multiple small correlation matrices are utilized to describe the correlation of the sample set. Additionally, we replace the original high-dimensional hyper-parameter tuning problem with a low-dimensional counterpart by learning the relationship between the hyper-parameters and the global sensitivity indices. Finally, we validate SGE-Kriging by means of numerical experiments with several benchmarks problems. The results show that the SGE-Kriging model features an accuracy and robustness that is comparable to the standard one but comes at much less training costs. The benefits are most evident in high-dimensional problems.

Suppose we observe a random vector $X$ from some distribution $P$ in a known family with unknown parameters. We ask the following question: when is it possible to split $X$ into two parts $f(X)$ and $g(X)$ such that neither part is sufficient to reconstruct $X$ by itself, but both together can recover $X$ fully, and the joint distribution of $(f(X),g(X))$ is tractable? As one example, if $X=(X_1,\dots,X_n)$ and $P$ is a product distribution, then for any $m<n$, we can split the sample to define $f(X)=(X_1,\dots,X_m)$ and $g(X)=(X_{m+1},\dots,X_n)$. Rasines and Young (2021) offers an alternative route of accomplishing this task through randomization of $X$ with additive Gaussian noise which enables post-selection inference in finite samples for Gaussian distributed data and asymptotically for non-Gaussian additive models. In this paper, we offer a more general methodology for achieving such a split in finite samples by borrowing ideas from Bayesian inference to yield a (frequentist) solution that can be viewed as a continuous analog of data splitting. We call our method data blurring, as an alternative to data splitting, data carving and p-value masking. We exemplify the method on a few prototypical applications, such as post-selection inference for trend filtering and other regression problems.

The recent emergence of reinforcement learning has created a demand for robust statistical inference methods for the parameter estimates computed using these algorithms. Existing methods for statistical inference in online learning are restricted to settings involving independently sampled observations, while existing statistical inference methods in reinforcement learning (RL) are limited to the batch setting. The online bootstrap is a flexible and efficient approach for statistical inference in linear stochastic approximation algorithms, but its efficacy in settings involving Markov noise, such as RL, has yet to be explored. In this paper, we study the use of the online bootstrap method for statistical inference in RL. In particular, we focus on the temporal difference (TD) learning and Gradient TD (GTD) learning algorithms, which are themselves special instances of linear stochastic approximation under Markov noise. The method is shown to be distributionally consistent for statistical inference in policy evaluation, and numerical experiments are included to demonstrate the effectiveness of this algorithm at statistical inference tasks across a range of real RL environments.  ( 2 min )

Representations of the world environment play a crucial role in artificial intelligence. It is often inefficient to conduct reasoning and inference directly in the space of raw sensory representations, such as pixel values of images. Representation learning allows us to automatically discover suitable representations from raw sensory data. For example, given raw sensory data, a deep neural network learns nonlinear representations at its hidden layers, which are subsequently used for classification at its output layer. This happens implicitly during training through minimizing a supervised or unsupervised loss. In this paper, we study the dynamics of such implicit nonlinear representation learning. We identify a pair of a new assumption and a novel condition, called the common model structure assumption and the data-architecture alignment condition. Under the common model structure assumption, the data-architecture alignment condition is shown to be sufficient for the global convergence and necessary for the global optimality. Moreover, our theory explains how and when increasing the network size does and does not improve the training behaviors in the practical regime. Our results provide practical guidance for designing a model structure: e.g., the common model structure assumption can be used as a justification for using a particular model structure instead of others. We also derive a new training framework, which satisfies the data-architecture alignment condition by automatically modifying any given training algorithm. Given a standard training algorithm, the framework running its modified version is empirically shown to maintain competitive test performances while providing global convergence guarantees for deep residual neural networks with convolutions, skip connections, and batch normalization with datasets, including MNIST, CIFAR-10, CIFAR-100, Semeion, KMNIST and SVHN.  ( 3 min )

One of the latest self-supervised learning (SSL) methods, VICReg, showed a great performance both in the linear evaluation and the fine-tuning evaluation. However, VICReg is proposed in computer vision and it learns by pulling representations of random crops of an image while maintaining the representation space by the variance and covariance loss. However, VICReg would be ineffective on non-stationary time series where different parts/crops of input should be differently encoded to consider the non-stationarity. Another recent SSL proposal, Temporal Neighborhood Coding (TNC) is effective for encoding non-stationary time series. This study shows that a combination of a VICReg-style method and TNC is very effective for SSL on non-stationary time series, where a non-stationary seismic signal time series is used as an evaluation dataset.  ( 2 min )

Several modern applications require the integration of multiple large data matrices that have shared rows and/or columns. For example, cancer studies that integrate multiple omics platforms across multiple types of cancer, pan-omics pan-cancer analysis, have extended our knowledge of molecular heterogenity beyond what was observed in single tumor and single platform studies. However, these studies have been limited by available statistical methodology. We propose a flexible approach to the simultaneous factorization and decomposition of variation across such bidimensionally linked matrices, BIDIFAC+. This decomposes variation into a series of low-rank components that may be shared across any number of row sets (e.g., omics platforms) or column sets (e.g., cancer types). This builds on a growing literature for the factorization and decomposition of linked matrices, which has primarily focused on multiple matrices that are linked in one dimension (rows or columns) only. Our objective function extends nuclear norm penalization, is motivated by random matrix theory, gives an identifiable decomposition under relatively mild conditions, and can be shown to give the mode of a Bayesian posterior distribution. We apply BIDIFAC+ to pan-omics pan-cancer data from TCGA, identifying shared and specific modes of variability across 4 different omics platforms and 29 different cancer types.  ( 2 min )

We present a mean-variance policy iteration (MVPI) framework for risk-averse control in a discounted infinite horizon MDP optimizing the variance of a per-step reward random variable. MVPI enjoys great flexibility in that any policy evaluation method and risk-neutral control method can be dropped in for risk-averse control off the shelf, in both on- and off-policy settings. This flexibility reduces the gap between risk-neutral control and risk-averse control and is achieved by working on a novel augmented MDP directly. We propose risk-averse TD3 as an example instantiating MVPI, which outperforms vanilla TD3 and many previous risk-averse control methods in challenging Mujoco robot simulation tasks under a risk-aware performance metric. This risk-averse TD3 is the first to introduce deterministic policies and off-policy learning into risk-averse reinforcement learning, both of which are key to the performance boost we show in Mujoco domains.  ( 2 min )

DeepTensor is a computationally efficient framework for low-rank decomposition of matrices and tensors using deep generative networks. We decompose a tensor as the product of low-rank tensor factors (e.g., a matrix as the outer product of two vectors), where each low-rank tensor is generated by a deep network (DN) that is trained in a self-supervised manner to minimize the mean-squared approximation error. Our key observation is that the implicit regularization inherent in DNs enables them to capture nonlinear signal structures (e.g., manifolds) that are out of the reach of classical linear methods like the singular value decomposition (SVD) and principal component analysis (PCA). Furthermore, in contrast to the SVD and PCA, whose performance deteriorates when the tensor's entries deviate from additive white Gaussian noise, we demonstrate that the performance of DeepTensor is robust to a wide range of distributions. We validate that DeepTensor is a robust and computationally efficient drop-in replacement for the SVD, PCA, nonnegative matrix factorization (NMF), and similar decompositions by exploring a range of real-world applications, including hyperspectral image denoising, 3D MRI tomography, and image classification. In particular, DeepTensor offers a 6dB signal-to-noise ratio improvement over standard denoising methods for signals corrupted by Poisson noise and learns to decompose 3D tensors 60 times faster than a single DN equipped with 3D convolutions.  ( 2 min )

A new data-driven method for operator learning of stochastic differential equations(SDE) is proposed in this paper. The central goal is to solve forward and inverse stochastic problems more effectively using limited data. Deep operator network(DeepONet) has been proposed recently for operator learning. Compared to other neural networks to learn functions, it aims at the problem of learning nonlinear operators. However, it can be challenging by using the original model to learn nonlinear operators for high-dimensional stochastic problems. We propose a new multi-resolution autoencoder DeepONet model referred to as MultiAuto-DeepONet to deal with this difficulty with the aid of convolutional autoencoder. The encoder part of the network is designed to reduce the dimensionality as well as discover the hidden features of high-dimensional stochastic inputs. The decoder is designed to have a special structure, i.e. in the form of DeepONet. The first DeepONet in decoder is designed to reconstruct the input function involving randomness while the second one is used to approximate the solution of desired equations. Those two DeepONets has a common branch net and two independent trunk nets. This architecture enables us to deal with multi-resolution inputs naturally. By adding $L_1$ regularization to our network, we found the outputs from the branch net and two trunk nets all have sparse structures. This reduces the number of trainable parameters in the neural network thus making the model more efficient. Finally, we conduct several numerical experiments to illustrate the effectiveness of our proposed MultiAuto-DeepONet model with uncertainty quantification.  ( 2 min )

Nonconvex penalties are utilized for regularization in high-dimensional statistical learning algorithms primarily because they yield unbiased or nearly unbiased estimators for the parameters in the model. Nonconvex penalties existing in the literature such as SCAD, MCP, Laplace and arctan have a singularity at origin which makes them useful also for variable selection. However, in several high-dimensional frameworks such as deep learning, variable selection is less of a concern. In this paper, we present a nonconvex penalty which is smooth at origin. The paper includes asymptotic results for ordinary least squares estimators regularized with the new penalty function, showing asymptotic bias that vanishes exponentially fast. We also conducted an empirical study employing deep neural network architecture on three datasets and convolutional neural network on four datasets. The empirical study showed better performance for the new regularization approach in five out of the seven datasets.  ( 2 min )

We propose a framework for the statistical evaluation of variational auto-encoders (VAEs) and test two instances of this framework in the context of modelling images of handwritten digits and a corpus of English text. Our take on evaluation is based on the idea of statistical model criticism, popular in Bayesian data analysis, whereby a statistical model is evaluated in terms of its ability to reproduce statistics of an unknown data generating process from which we can obtain samples. A VAE learns not one, but two joint distributions over a shared sample space, each exploiting a choice of factorisation that makes sampling tractable in one of two directions (latent-to-data, data-to-latent). We evaluate samples from these distributions, assessing their (marginal) fit to the observed data and our choice of prior, and we also evaluate samples through a pipeline that connects the two distributions starting from a data sample, assessing whether together they exploit and reveal latent factors of variation that are useful to a practitioner. We show that this methodology offers possibilities for model selection qualitatively beyond intrinsic evaluation metrics and at a finer granularity than commonly used statistics can offer.  ( 2 min )

We investigate and leverage a connection between Differential Privacy (DP) and the recently proposed notion of Distributional Generalization (DG). Applying this connection, we introduce new conceptual tools for designing deep-learning methods that bypass "pathologies" of standard stochastic gradient descent (SGD). First, we prove that differentially private methods satisfy a "What You See Is What You Get (WYSIWYG)" generalization guarantee: whatever a model does on its train data is almost exactly what it will do at test time. This guarantee is formally captured by distributional generalization. WYSIWYG enables principled algorithm design in deep learning by reducing $\textit{generalization}$ concerns to $\textit{optimization}$ ones: in order to mitigate unwanted behavior at test time, it is provably sufficient to mitigate this behavior on the train data. This is notably false for standard (non-DP) methods, hence this observation has applications even when privacy is not required. For example, importance sampling is known to fail for standard SGD, but we show that it has exactly the intended effect for DP-trained models. Thus, with DP-SGD, unlike with SGD, we can influence test-time behavior by making principled train-time interventions. We use these insights to construct simple algorithms which match or outperform SOTA in several distributional robustness applications, and to significantly improve the privacy vs. disparate impact trade-off of DP-SGD. Finally, we also improve on known theoretical bounds relating differential privacy, stability, and distributional generalization.  ( 2 min )

Although evaluation of the expectations on the Ising model is essential in various applications, this is frequently infeasible because of intractable multiple summations (or integrations). Spatial Monte Carlo integration (SMCI) is a sampling-based approximation, and can provide high-accuracy estimations for such intractable expectations. To evaluate the expectation of a function of variables in a specific region (called target region), SMCI considers a larger region containing the target region (called sum region). In SMCI, the multiple summation for the variables in the sum region is precisely executed, and that in the outer region is evaluated by the sampling approximation such as the standard Monte Carlo integration. It is guaranteed that the accuracy of the SMCI estimator is monotonically improved as the size of the sum region increases. However, a haphazard expansion of the sum region could cause a combinatorial explosion. Therefore, we hope to improve the accuracy without such region expansion. In this study, based on the theory of generalized least squares, a new effective method is proposed by combining multiple SMCI estimators. The validity of the proposed method is demonstrated theoretically and numerically. The results indicate that the proposed method can be effective in the inverse Ising problem (or Boltzmann machine learning).  ( 2 min )

Just based on the estimated running costs of GPT3, and then whatever profit gets applied on top of that, are there any estimates for what openai will eventually charge for image generation? submitted by /u/EugeneJudo [link] [comments]  ( 1 min )

I don't know what would be the best place to post this. But I am having trouble uploading an Overleaf manuscript to arXiv based on the CVPR 2022 template. I am getting the following error. Does anyone have any ideas? ​ https://preview.redd.it/y9jq0rbysds81.png?width=1614&format=png&auto=webp&s=16be3d32468837f649e846ec8a309dab2854c762 submitted by /u/avd4292 [link] [comments]

Paper: https://arxiv.org/abs/2204.00598 https://socraticmodels.github.io/ Twitter: https://twitter.com/andyzengtweets/status/1512089759497269251 Abstract: " Large foundation models can exhibit unique capabilities depending on the domain of data they are trained on. While these domains are generic, they may only barely overlap. For example, visual-language models (VLMs) are trained on Internet-scale image captions, but large language models (LMs) are further trained on Internet-scale text with no images (e.g. from spreadsheets, to SAT questions). As a result, these models store different forms of commonsense knowledge across different domains. In this work, we show that this model diversity is symbiotic, and can be leveraged to build AI systems with structured Socratic dialogue -- in whi…  ( 1 min )

I have two years of time-series data taken from two sensors which I have split into 80/10/10 non-overlapping train/val/test splits. The task is to denoise one sensor data into another and I am handling it as a regression problem. I am following this website and considered an already published model (5 convolutional and 1 fully connected layer) which is trained on a similar dataset and same task. For the sake of sanity check, as per the website, I have trained the model on a subset of trainset (3 months) and tried to overfit it (while evaluating on complete val set), which works fine. However, I am not sure what to do next from this point on? Shall I just train on the complete trainset now? Or do I increase the layers or play with other hyper params to find more details about my regression problem/data? I would really appreciate your comments. Thank you. PS. The target value is sparse i.e. more than 85% of the time it is zero. submitted by /u/muaz_usmani [link] [comments]  ( 1 min )

I'm keen to hear from people who have made the transition from ML Research/Engineering positions to software engineering roles (or who are considering it). What were your reasons for doing it and did you regret it? I see so many articles about transition from software to ML but none about going the opposite direction. Context: I've been working as an ML Engineer for a little over a year, and I'm just... not enjoying it. I want to love my job so badly as I like my boss, my colleagues, and the company (and I'm paid quite well for my level), but I just don't. I feel like the type of work I'm doing is not very smart and yet it's extremely draining: I spend so many hours just looking at loss curves, tweaking features and parameters. I'm somehow bored and stressed at the same time, because I don't enjoy the work and yet I feel the pressure to produce good models, and when they don't work as expected I can't help but take it personally as if if I just tried hard enough they would work. I find that the days were I end up having to take care of more purely engineering tasks I just have a lot more fun and I finish the day more satisfied and less drained. I think I just want to build something instead of spending hours banging my head against shit data. I would love to hear from people who feel or have felt the same way because whenever I speak about this with friends who are in ML they look at me like I'm a lunatic for wanting to leave it for software engineering. I'm obviously aware that swe roles are not all fun and games, but I just feel like there's been an excessive push for so many people to move to ML as it's "cool" and "smart" when in reality they're just different things who are going to suit different people. submitted by /u/hedy-m [link] [comments]  ( 5 min )

The 2022 ICLR conference at the end of this month is virtual and costs $100 to attend. I was thinking of attending for networking opportunities but I’m not sure. Is it a good idea to go for it? submitted by /u/sybar142857 [link] [comments]  ( 1 min )

The online triplet mining strategy is more efficient than the offline one. It implies "getting a batch of n samples and their associated labels, and form triplets on the fly." Here is an article about Triplet vs. Contrastive Loss comparison and its efficient implementation. I would like to know your feedback. submitted by /u/devzaya [link] [comments]

Hello everybody, I'd like to share the latest machine learning project of mine. It allows one to generate animated characters in the style of old video game consoles. Here are some examples. I would appreciate any feedback. https://i.redd.it/sig6ilpi8bs81.gif https://i.redd.it/xaf906qi8bs81.gif https://i.redd.it/8v7lz2qi8bs81.gif submitted by /u/ie9res [link] [comments]

Hey ML people, what is your favorite annotation format for image bounding boxes/labels? I know coco is very popular, we are rethinking parts of our data infrastructure wondering what everyone is using. Our platform hosts hundreds of millions of images. Ideal format would support running queries on data stored in a Data lake If the format supports 3D annotation types that is even better. Thanks for your insights in advance. submitted by /u/mmuppidi [link] [comments]  ( 1 min )

New version of paper is linked to in the DALL-E 2 blog post and also here (pdf file format). Tweet announcing updated paper. Older version of paper (pdf file format). Original Reddit post. submitted by /u/Wiskkey [link] [comments]  ( 1 min )

Hi, I made a video explaining Dense Passage Retriever(DPR) paper. We specifically explain the End to End QA system suggested in the latter part of the paper which discusses how to build an Open-QA system using dense retrievers. DPR was one of the first papers that discussed building dense retrievers using QA pairs only and didn't require a big pretraining computational setup like ORQA or REALM. It is currently used in a lot of places as a dense retriever. You can find Hugginface and Haystack implementations also. This video is part of a series on Open-QA using dense retrievers. We have made 2 videos on DPR. In the latter, we discuss how to build a dense retriever from scratch. Thanks for the support and it would be great if you could give any feedback. https://www.youtube.com/watch?v=rvcyyJNjPU0 submitted by /u/infiniteakashe [link] [comments]  ( 1 min )

Hi. I'm looking for existing computer vision papers /networks that can process variable input resolution. Can anyone share me with similar works? For example, a network/layer N can take both inputs with H*W and 2H*2H individually and give correct prediction. One of them I know is ROI pooling used in Faster RCNN. Thanks very much. submitted by /u/vincent341 [link] [comments]  ( 1 min )

Hey, I'm looking to transition from my current role as a data scientist to one that has a machine learning engineering focus. I was wondering if anyone could provide insights into how they plan their day, or what activities you do throughout the day/week. I'd be particularly interested to understand the balance between deploying models/writing production worthy code and your time spent learning/developing given the field is moving so fast. submitted by /u/MenArePigs69 [link] [comments]  ( 4 min )

I am currently sitting at a difficult machine-learning problem that I have found no literature on how to solve it. I am given n datapoints x_1,...,x_n that are ordered according to a ranking preference rank(x_1)<rank(x_2)<...<rank(x_n). I am assuming there exists a function f, such, that f(x_i)<f(x_i+1). I am now searching a Bayesian non-parametric model that gives the posterior probability of functions f that abide f(x_i)<f(x_i+1), so that i can estimate the relative rank preferences at new points. I have tried out a few things. The naive approach is using a GP prior on f. Unfortunately, computing the posterior distribution p(f(x_1), ... f(x_n)| f(x_1)<...<f(x_n)) has no closed form solution (it is a normal distribution with N linear constraints, which is absolutely terrible to sample from). This makes computing conditional distributions for predictions very challenging. I am currently approximating the solution by using a GP regression model with label y_i = rank(x_i)=i. But this is systematically under-estimating the shape-variation, due to the fact that it adds the assumption that function values between ranks are equidistant. Is there any known approach how to do this? submitted by /u/Ulfgardleo [link] [comments]  ( 2 min )

From the webpage: We present results on video generation using diffusion models. We propose an architecture for video diffusion models which is a natural extension of the standard image architecture. We show that this architecture is effective for jointly training from image and video data. To generate long and higher resolution videos we introduce a new conditioning technique that performs better than previously proposed methods. We present results on text-conditioned video generation and state-of-the-art results on an unconditional video generation benchmark. Paper: https://arxiv.org/abs/2204.03458 https://video-diffusion.github.io/ submitted by /u/hardmaru [link] [comments]  ( 1 min )

How to decide if a paper is appropriate for a specific venue? Moreover, how would you categorize the difference between a good NiPs publication and a good CvPR or ICCV publication? submitted by /u/LifeguardDismal142 [link] [comments]  ( 1 min )

​ RadamWarmup + CosineAnnealingLR + StepLR Colab Link pytorch_warmup v0.1.0 was released. submitted by /u/TonyY_RIMCS [link] [comments]

In their 2019 review article in Nature Machine Intelligence, Neftci and Averbeck point out, “Most work in biological systems has focused on simple learning problems… where flexibility and ongoing learning are important, similar to real-world learning problems. In contrast, most work in artificial agents has focused on learning a single complex problem in a static environment.” Are there RL approaches designed to handle dynamic environments with changing reward functions? I did find this earlier post, but thought I'd ask if anyone had other suggested lines of reading. Thanks! submitted by /u/Careless-Argument-37 [link] [comments]  ( 1 min )

I want to give my AI as many information as possible. Can there be an Issue with a too large observation space? submitted by /u/Willing-Classroom735 [link] [comments]  ( 1 min )

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I am working on a project in which a robots learns its motion. for example Bipedal robot learns to walk on a straight line by learning to adjust the torque and angular velocity of each joint. However, the robot I am working on has complex architecture. It has 10 joints instead of 2, most importantly all of these joints work simultaneously and coherently to produce a desired motion. The Problem I am facing is that, the robot has ten joints and each joint can move between -450 to +450 for simplicity let me define State and Actions of the system State = -450 to +450 -------------> normalization ------------------> -10 to 10 Actions = choose an angle between -10 to 10 for each joint Total Action space for each State at each time step = 10 (no of joints each can move between -10 to 10 at each time step) * 360 (total time steps for a single motion) = 3600 (Output: No of angles required to generate a motion) I am using TD3 to solve this conundrum. The Action space is too large how can I reduce the action space? submitted by /u/SAM_Baloch [link] [comments]  ( 2 min )

I am doing a project and there is a problem with dynamic action space A complete action space can be divided into four parts. In each state, the action to be selected is one of them For example, the total discrete action space length is 1000, which can be divided into four parts, [0:300], [301:500],[501:900],[901:1000] For state 1, action_ space is [0:300], State2, action_ space is [301:500], etc For this idea, I have several ideas at present: There is no restriction at all. The legal actions of all States are [1:1000], but it may take longer train time and there is not much innovation Soft constraint, for example, if state1 selects an illegal action, such as one action in [251: 500], reward gives a negative value, but it is also not innovative Hard constraint, use action space mask in each state, but I don't know how to do it.. Is there any relevant article？ It is directly divided into four action spaces and uses multi-agent cooperative relationship learning ​ Any suggestions？ thanks！ submitted by /u/RangerWYR [link] [comments]  ( 2 min )

Hi everyone, i have to define a project for my master degree, so i'm looking for the best papers published since 2018-2019 until now in Reinforcement Learning . Do you have any suggestions, titles or projects that i can check? submitted by /u/acaviedes15 [link] [comments]  ( 1 min )

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Remember using a website like a year ago, where you could put in 2 or more images, and it would sort of make a transition between the two with AI. Then you could export the video and such. You could also very extensively edit human faces and change small features on a scale from 1-100. The features where incredibly specific like brow bone and nasal bridge. If anyone has the website I would appreciate it!! submitted by /u/yungbenz0_bajs [link] [comments]  ( 1 min )

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What in your opinion is the best GAN for tabular-data. Please include any references if you have any. submitted by /u/ily_jk [link] [comments]

Hi guys, we've built a plugin that seamlessly reads MLflow logs and provides a beautiful UI to compare multiple runs with just a few clicks. You can: filter runs with a super versatile fully pythonic search group and aggregate your metrics / images We are trying make it work seamlessly with MLflow and complement its other awesome features 🎉 Here is more info about it https://aimstack.io/aimlflow Would love your feedback!! submitted by /u/ManeSa [link] [comments]  ( 1 min )

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​ https://preview.redd.it/e2q3jit3c8s81.png?width=1024&format=png&auto=webp&s=837aa6256647df6fb8777a02b04313a38428f573 The most common diagnostic imaging test conducted in emergency rooms is chest radiography. Providing automated preliminary read helpers to physicians might speed up surgery, enhance accuracy, and lower healthcare costs. An artificial intelligence tool that interprets chest X-rays without the intervention of a radiologist received regulatory approval in the European Union this week, marking a first for a wholly autonomous medical imaging AI, according to ‘Oxipit‘, the developer of this tool. It’s a watershed moment for AI, and it’s more than likely to spark debate, given that radiologists have spent the last few years working to fully automate parts of their jobs. Continue Reading submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

Posted by Harsh Mehta, Software Engineer, Google Research Matrix factorization is one of the oldest, yet still widely used, techniques for learning how to recommend items such as songs or movies from user ratings. In its basic form, it approximates a large, sparse (i.e., mostly empty) matrix of user-item interactions with a product of two smaller, denser matrices representing learned item and user features. These dense matrices, in turn, can be used to recommend items to a user with which they haven't interacted before. Despite its algorithmic simplicity, matrix factorization can still achieve competitive performance in recommender benchmarks. Alternating least squares (ALS), and especially its implicit variation, is a fundamental algorithm to learn the parameters of matrix factorization…  ( 9 min )

A team of scientists have created a new AI-based tool to help lock up greenhouse gases like CO2 in porous rock formations faster and more precisely than ever before. Carbon capture technology, also referred to as carbon sequestration, is a climate change mitigation method that redirects CO2 emitted from power plants back underground. While doing Read article > The post Rock On: Scientists Use AI to Improve Sequestering Carbon Underground appeared first on NVIDIA Blog.  ( 4 min )

In many industries, it’s critical to extract custom entities from documents in a timely manner. This can be challenging. Insurance claims, for example, often contain dozens of important attributes (such as dates, names, locations, and reports) sprinkled across lengthy and dense documents. Manually scanning and extracting such information can be error-prone and time-consuming. Rule-based software […]  ( 7 min )

Amazon Kendra is a highly accurate and easy-to-use intelligent search service powered by machine learning (ML). Amazon Kendra offers a suite of data source connectors to simplify the process of ingesting and indexing your content, wherever it resides. For many organizations, Box Content Cloud is a core part of their content storage and lifecycle management […]  ( 6 min )

The Hilbert transform of a function f(t) is a function fH(x) defined [1] by The integral must be interpreted in the sense of the Cauchy principal value: The integrand is not absolutely integrable because of the singularity at x and so the value of the integral depends on how you handle the singularity. The Cauchy […] Hilbert transform and Mathematica first appeared on John D. Cook.  ( 2 min )

The plot below is of a meromorphic function f(z). That is, the function f(z) is analytic except possibly at poles, and the colors represent the phase angles, the values of θ if you write the function values in polar form. What is the value of the integral where C is the perimeter of the square? […] Visual integration first appeared on John D. Cook.  ( 2 min )

Hi, I made a video explaining Dense Passage Retriever(DPR) paper. We specifically explain the End to End QA system suggested in the latter part of the paper which discusses how to build an Open-QA system using dense retrievers. DPR was one of the first papers that discussed building dense retrievers using QA pairs only and didn't require a big pretraining computational setup like ORQA or REALM. It is currently used in a lot of places as a dense retriever. You can find Hugginface and Haystack implementations also. This video is part of a series on Open-QA using dense retrievers. We have made 2 videos on DPR. In the latter, we discuss how to build a dense retriever from scratch. Thanks for the support and it would be great if you could give any feedback. https://www.youtube.com/watch?v=rvcyyJNjPU0 submitted by /u/infiniteakashe [link] [comments]  ( 1 min )

AI has been making a lot of noise of late, especially in the context of software development. Of course, this topic is quite wide, but in this article, we shall focus our attention on AI-driven automation testing. Let us start with understanding what is AI and automation testing. Automation testing refers to the process of… Read More »Artificial Intelligence: Benefits for Automation Testing The post Artificial Intelligence: Benefits for Automation Testing appeared first on Data Science Central.  ( 3 min )

Interested in the future of autonomous vehicles? Want to know more about the impacts of this technology? Join us on April 16-17th at the 2022 National Autonomous Vehicle Expo to discover the engineering, ethics, and policymaking of this emerging technology. The virtual expo consists of speaker and workshop sessions led by industry-leading companies, such as NVIDIA, Waymo, and Motional, as well as distinguished programs/organizations like MIT Beaverworks and InspiritAI. You will also have the opportunity to compete in our hackathon, where you can win a variety of cool prizes! Even if you don't participate in the hackathon, there will be free merchandise and giveaways throughout the expo! To register and/or view more information about the event, head over to avexpo.org. For hackathon-specific registration, you can visit our devpost at https://autonomous-vehicle-expo.devpost.com/. Hope to see you all there! ​ https://preview.redd.it/qgfx3sv837s81.png?width=1080&format=png&auto=webp&s=ed19d68bdff274de188deaa8f4338c864943b508 https://preview.redd.it/a4kbsrv837s81.png?width=1080&format=png&auto=webp&s=6a19347b708d822d8dca226beb82cbdc73ffbb87 https://preview.redd.it/s9nghsv837s81.png?width=1080&format=png&auto=webp&s=cf32712b9985564b455be53e02fd00589725ad2c submitted by /u/avexpo22 [link] [comments]  ( 1 min )

Hi! I am new to the community, and was wondering what y'all's favorite resources were to learn about cognitive theories and how they will shape future AI advancements. YouTube channels would be great. submitted by /u/Apprehensive-Candy97 [link] [comments]

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I wanna create a bot for a game which basically is: get resources, craft itens, sell then. The problem is, some itens has different qualities, and I wanna automatize this process, to identify the good stuff to keep, and sell the bad stuff. What's the best way to do that? I work with desktop systems, so i'm not familiar with this kind of stuff, but I usually read about python and some frameworks, what do you guys recommend me to start? submitted by /u/AbbathDoom [link] [comments]  ( 1 min )

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Hello! What do you guys think are other "technology" fields that would be good to study with AI? It is okay as long as it is "tech." What would be the tech field that would be beneficial in the future? My goal is to make a self-aware AI (AGI). I was always fascinated about AI since my childhood, that's why I'm going to pursue this field. Also, I am currently studying Game Development to make a VR Game that hopefully will have humanlike AI in it. I have read a LOT of articles about the future of AI, and Cybersecurity keeps popping up because superintelligent AI needs to be CONTROLLED from hackers (based on the articles) otherwise it is over. What do you guys think would be the tech field that will bring the most changes in the future? submitted by /u/ThatOneEpicAstronaut [link] [comments]  ( 1 min )

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We keep on hearing about how artificial intelligence and machine learning is going to revolutionise Medicine. But what’s hype, and what’s realistic? And how can you get involved? The first step is to understand the technology - where it’s well-suited to healthcare (and where it isn’t). When it comes to health care, especially for life and death situations AI has made things very easy for us. However, it is still expected to drastically change the way medicine is practised. It will also replace the surgeries done by the doctors with the surgeries done using Artificial intelligence, making diagnosing complex diseases, genetic issues and many other health problems extremely easy in the future. Here are the best Artificial Intelligence courses for healthcare you can learn in 2022. submitted by /u/maneesh123456 [link] [comments]  ( 1 min )

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I've read online that a career in AI policy and strategy is heavily needed and is actually ranked as the number one problem in the future by 80,000 hours. I am choosing which undergraduate degree to pursue in the fall and I'm not sure the best pathway to pursue to work in this field in an extremely high level position. an economics degree? Computer science degree? AI degree? should I pursue one subject until I get a PhD in it or mix with other degrees/certificates? is it a straight forward pathway focused on one subject where I only work in one subject field or is it necessary to pursue and work in other fields as well, what are the typical steps? Also if there is anything else that would be helpful on the pathway or anything you would recommend please let me know. submitted by /u/Key-Lawyer-7586 [link] [comments]  ( 2 min )

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Hi all! I am a beginner in RL field and am trying to implement the RL algorithm in the following paper : [1912.04321] Learning to Code: Coded Caching via Deep Reinforcement Learning (arxiv.org) In short, we are trying to achieve the minimum number of transmission of bits from the server to all users Now, after 500 episodes of training the number of transmissions does decrease. But when I implement the same actor critic algorithm this does not happen. In fact, the results seem to be completely random. Here is the plot of the same : https://preview.redd.it/yve3cay7l6s81.png?width=745&format=png&auto=webp&s=36bf4f0aa813a30024128d797acde3b8adc2df30 Although my training parameters are slightly different, I can't understand why this would happen. I used the parameters and pseudo code from this paper: A Deep Reinforcement Learning Approach for Shared Caching | IEEE Conference Publication | IEEE Xplore - which is an extension of the link at the top. ​ Attaching link to my code: https://www.kaggle.com/samarthtiwari123/rl-for-coded-caching Any help would be helpful!! Thanks in advance submitted by /u/samt_123 [link] [comments]  ( 1 min )

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Naive question: if I define my RL model as a combination of different classes (one class that preprocesses the observation, one class that processes the observation, one class that outputs the actions, etc.), is this going to affect the flow of gradients in PyTorch? The alternative would be to create only one class in which I combine everything submitted by /u/No_Possibility_7588 [link] [comments]  ( 1 min )

In the hyper parameter section of the paper, it is written that step size of Adam is varied according to KL divergence. So I wanted to know is KL divergence the correct metric to be used for observing the learning progress because we have many states for which probabilites of a particular action is either increased or decreased thus taking average KL mixes up a lot of things. submitted by /u/Better-Ad8608 [link] [comments]  ( 1 min )

We’re finding that our network is returning a tensor of NaNs towards the end of training. Adding weight decay solves this issue but reduces learning, was wondering if anyone else had experience with vanishing gradients in off-policy methods or any insight? submitted by /u/TerrificJam [link] [comments]  ( 1 min )

Has anyone ever come across seemingly chaotic self attention maps during visualization. If your model is performing well but no insights can be gleaned from the visualization how do you explain it in a paper? submitted by /u/LifeguardDismal142 [link] [comments]  ( 1 min )

Here's the blogpost estimating the cost. What would it cost you to train PaLM using cloud computing (and you're not Google)? Something around $9M to $17M. submitted by /u/cirqe [link] [comments]  ( 1 min )

I'm working on a ML project and I'm working with a dataset with 20 columns, for feature selection I just removed one column one by one and looked at the error of the ML outputs for each, then saw when what column is removed gives a lower error and kept repeating that but that didn't seem to help the model at all and the error went down very little. Is this an okay way of doing feature selection is there another way that gives better results. I tried PCA and LDA and Pearson Correlation method as well in Python and that didn't seem to help or is this the best I could do. Thanks! submitted by /u/ihshosv [link] [comments]  ( 1 min )

This is a two part question: If a neural network can overfit the a large dataset is this a sign that a neural network has high learning capacity? If a neural network can overfit a dataset with substantially less parameters than other neural networks developed for the same learning task is this a sign that the neural network has a high learning capacity relative to other datasets? submitted by /u/LifeguardDismal142 [link] [comments]  ( 1 min )

I'm doing research on the use of synthetic data for a computer vision task and i have generally always tried to train in a mixed setting from scratch, but i have noticed that in similar papers, researchers always pretrain on synth first and then finetune on real data. Is there a logic behind that? Should i expect better results by finetuning? submitted by /u/TheManveru [link] [comments]  ( 2 min )

Hi Everyone, We have recently published the code for our AISTATS 2022 paper - Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data ​ Video Segmentation Example In our work, we have proposed a solution for clustering streaming data. Unlike 'standard' clustering scenarios, in the streaming case the data stream is possibly infinite, you cannot backtrack to previously processed points, and the data statistics are dynamic and change over time. Our solution is based on the Dirichlet Process Mixture Model (DPMM), can work with different types of observations, and is very fast, outperforming other methods both in the quality of the results and the speed with which it achieves them. It can even be distributed across several processes and/or machines! Paper: https://dinarior.github.io/papers/Dinari_AISTATS_streaming.pdf Code (Julia Package): https://github.com/BGU-CS-VIL/DPMMSubClustersStreaming.jl Code (Python wrapper): https://github.com/BGU-CS-VIL/dpmmpythonStreaming Notebook (Julia) for creating the video: https://nbviewer.org/github/BGU-CS-VIL/DPMMSubClustersStreaming.jl/blob/main/examples/VideoSeg.ipynb submitted by /u/dinarior [link] [comments]  ( 1 min )

I am thinking of building a graph classifier that takes in graphs and labels the incoming graph. The dataset of interest to me is RadGraph: https://arxiv.org/abs/2106.14463 The issue I am having is that the graphs in RadGraph are disconnected in nature (on average 20 disconnected components), making it difficult for the various graph encoders I am aware of to do a good job classifying the graphs. submitted by /u/AICoderGamer [link] [comments]  ( 1 min )

Implementation here Looks like they manually calculate the gradient? I'm very curious how much of a difference this makes! submitted by /u/fasttosmile [link] [comments]  ( 1 min )

https://outsystems-ai-reading-group.github.io/ submitted by /u/JClub [link] [comments]

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Are there any free to use datasets that contain image/annotation pairs in the style OpenAI used to train the DALL-E models? Pretty inspired by DALL-E 2 and think it would be cool to create a tiny less powerful replication submitted by /u/puppet_pals [link] [comments]  ( 1 min )

TLDR: TensorFlow AutoGraph unwraps native Python ranges, baking each value into the graph. This can be an unexpected cause of graph size explosion. This recently caused an issue in my project, so I thought I'd share some more details: https://lukewood.xyz/blog/to-unroll-or-to-not-unroll submitted by /u/puppet_pals [link] [comments]  ( 1 min )

Our work "AdaTime: A Systematic Evaluation of Domain Adaptation Algorithms on Time Series Data" is now public. We provide a benchmarking framework named "AdaTime" to fairly evaluate Unsupervised domain adaptation (UDA) approaches on time-series data. We find that UDA approaches proposed for visual data can be directly applied to time-series data, and still achieve excellent performance, even better than methods specially proposed for time-series UDA. Se were impressed by the consistently superior performance of "DIRT-T" method on all the datasets. We provide the code publicly on github https://github.com/emadeldeen24/AdaTime submitted by /u/emad_eldeen [link] [comments]  ( 1 min )

Into machine learning? Want a chance to earn a new MacBook Pro? Check out the Denoising ShabbyPages competition! The ShabbyPages dataset is being produced as a way to help train, test, and calibrate computer vision machine learning algorithms designed for working with documents. Enter the competition by training a model to remove the noise, and be awarded a MacBook Pro or some swag in the process! Check out the short paper introducing the dataset, and learn more about the competition at denoising-shabby.com. submitted by /u/proofconstruct [link] [comments]  ( 1 min )

Hi Everyone! We have released the preprint and google colab demo for our paper FaceSigns. FaceSigns embeds a secret bit-string as a semi-fragile watermark in the image pixels. The message is recoverable if benign image operations such as color/contrast adjustment, JPEG compression, Instagram filters are applied. However, the message cannot be decoded if the image is facially tampered (eg. DeepFake manipulation) . This selective fragility allows reliable detection of DeepFake manipulations applied on images signed using FaceSigns. Try out our google colab demo to see message encoding and decoding using FaceSigns! Paper: https://arxiv.org/abs/2204.01960 Project Webpage: https://shehzeen.github.io/facesigns Demo: https://github.com/paarthneekhara/FaceSignsDemo submitted by /u/LynxCompetitive7637 [link] [comments]  ( 1 min )

Hi guys! I work in house and I’m part of our Google search team. Our ad spend is pretty large (9 figures per year, in USD). We build/manage stuff at scale using SQL, R, Javascript, and so on. So everything is pretty much “big data” in flavour. Lately I’ve been more and more interested in data science, and I’m looking to take things to the next level by incorporating machine learning into our workflow. I’d really love to build some useful machine learning models using popular Python libraries such as Pandas, SciKit Learn, NumPy, TensorFlow, PyTorch, and so on. Any suggestions on cool, and most importantly useful machine learning models I could build? (By “useful”, I mean something that could help increase the profits.) I think some classification, predictive, or recommender models would be great to start with. Cheers! 😄 submitted by /u/TropicalBound111 [link] [comments]  ( 2 min )

Posted by Tal Remez, Software Engineer, Google Research and Micheal Hassid, Software Engineer Intern, Google Research Recent years have seen a tremendous increase in the creation and serving of video content to users across the world in a variety of languages and over numerous platforms. The process of creating high quality content can include several stages from video capturing and captioning to video and audio editing. In some cases dialogue is re-recorded (referred to as dialog replacement, post-sync or dubbing) in a studio in order to achieve high quality and replace original audio that might have been recorded in noisy conditions. However, the dialog replacement process can be difficult and tedious because the newly recorded audio needs to be well synced with the video, requiring …  ( 7 min )

‍What is the shortest distance between two points? A straight line of course. What if there are multiple points? Then, it depends.  A job executed in response to a user action – refreshing a dashboard, aggregating data, building a report, developing an ML algorithm, performing analytics – all require multiple hops through the data ecosystem.… Read More »Data Observability: Cracking the Code The post Data Observability: Cracking the Code appeared first on Data Science Central.  ( 5 min )

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Regular expressions can do a lot of tasks in practice that they cannot do in theory. That’s because a particular application of regular expressions comes with context and with error tolerance. For example, much has been said about how regular expressions cannot parse HTML. This is strictly true, but it says nothing about how well […] Regular expressions and successive approximation first appeared on John D. Cook.  ( 3 min )

GeForce NOW is about bringing new experiences to gamers. This GFN Thursday introduces game demos to GeForce NOW. Members can now try out some of the hit games streaming on the service before purchasing the full PC version — including some finalists from the 2021 Epic MegaJam. Plus, look for six games ready to stream Read article > The post Try This Out: GFN Thursday Delivers Instant-Play Game Demos on GeForce NOW appeared first on NVIDIA Blog.  ( 3 min )

To my understanding, the cost function is the error between predicted state value and real state value. So if I use a neural network as my dynamics model(unknown true dynamics), the MPC cost function is equivalent to NN’s loss function? submitted by /u/Blasphemer666 [link] [comments]  ( 1 min )

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Hey guys I'm curious which environments you use to benchmark your standard RL algorithms. I typically use some environments from the OpenAI Gym or the DM control suite but benchmarking all my implementations against all environments for multiple seeds would take forever. Are there some of their environments you particularly like for benchmarking? submitted by /u/NiconiusX [link] [comments]  ( 1 min )

In the PPO paper it is stated that we have to collect trajectories of length T from N different workers. Suppose I am not using multiple workers then I have to collect episodes N times of fixed length T. But these episode lengths are variable i.e. some episodes end much before T and some much after T. So my question is how do we calculated advantage because according to the PPO paper, for generalized advantage estimate, we have to observe the reward of terminal state. ​ So how should I calculate GAE in this ? submitted by /u/Better-Ad8608 [link] [comments]  ( 1 min )

I could not find an answer to a question hanging around in my head for a while. Suppose we have some data, if we build up an MDP to capture actions + state dynamics. Would the optimal policy win state-of-art RL algorithms? Edit: If that is the case, why would the community bothers with learning algorithm since finding the model of dynamics is the key? ​ submitted by /u/musicinthedark [link] [comments]  ( 2 min )

Hello, I am relatively new in the area of machine learning/reinforcement learning. I have this basic question regarding practical implementations. I just want to know, what does it look like the output of a reinforcement learning agent/algorithm in practice? Is it like a 'look-up table' that will set the weights/parameters of the ML model based on the input data? Note that I am asking after the offline training of the agent. How to implement the trained agent in practice, like in an embedded system? Do you guys have references or clues to help me to clarify? BR submitted by /u/b0bzera [link] [comments]  ( 1 min )

Hello! I am using SAC(Soft Actor-Critic) to complete a reinforcement learning task with only four steps, each action is from one of four different action spaces. These four action spaces are essentially the same, and they are all chemical compound. I just want the agent to take different types of compounds at each step. I have the following questions： Whether the different four steps can be trained? In fact, there is a paper that only has four steps in a reinforcement learning process, but his action space only has one discrete action space. Is there any article I can learn from? because I know that for the handle control of the game, there are usually multiple discrete action spaces, but each discrete space dimension of my task is larger, such as [800, 700, 500, 600] Thanks！ submitted by /u/RangerWYR [link] [comments]  ( 1 min )

quick question submitted by /u/Asleep_Donut1382 [link] [comments]

At my company we use RL to solve our problem. The thing is : our problem is rather complex, and this is the core of our product (so clients rely on the results produced). In order to reach satisfying results despite an agent that doesn't learn very well, we use sampling at inference time : instead of taking the best trajectory according to the agent, we take X trajectories and keep only the one with the best reward. This seems completely fine at first (similar things are done in NLP for example, with beam search), but in our case the sampling size is huge : 1024. Usually when using beam search, we use maybe a beam size of 6. Maybe 10 if you have good hardware ? ​ Now, the agent seems to be learning : the mean return is slightly increasing over time, the entropies for the actions are steadily decreasing, etc... Now the goal of the ML team is to improve agent's learning to decrease the sampling size at inference time (because it's costly to run 1024 trajectories through the environment...). But whatever we try, the improvements are not reflected (we compare all our experiments with 1024 sampling in order to see what the customers will see). ​ IMO this is because our sampling size is way too huge, even a random agent can produce okay-ish results... Is my intuition the right one ? submitted by /u/dummy-gummy [link] [comments]  ( 2 min )

I'm in the last semester of my undergraduate degree; over the past couple of weeks, I've been trying to brainstorm ideas that I would like to pursue in my graduate research career. I'm interested in the emergence of language in multi-agent reinforcement environments but I can't see how this would be important down the line when there are large language models that are completely dominating language and communication. Should this stop me from pursuing this idea or should I let my interest in the idea take precedence? submitted by /u/clarky103 [link] [comments]  ( 1 min )

Out of curiosity, how long would it take to implement a paper like this one? https://arxiv.org/abs/2104.07750 It has PPO agents in MARL, all of them with multihead attention performed on the observation, in such a way that an attention map is created for each agent. This attention map has information about how strongly each agent is attending to various elements of the environment. With KL divergence, the agents are rewarded for minimizing the difference between their attention maps. submitted by /u/No_Possibility_7588 [link] [comments]  ( 1 min )

Predicting longtail events can be one of the more challenging ML tasks. Last year my team published a blog article where we improved DoorDash’s ETA predictions by 10% by tweaking the loss function with historical and real-time features. I thought members of the community would be interested in learning how we improved the model even more by using Gamma distribution-based inverse sampling approach to loss function tunning. Please check out the new article for all the technical details and let us know your feedback on our approach. https://doordash.engineering/2022/04/06/using-gamma-distribution-to-improve-long-tail-event-predictions/ submitted by /u/pmp-dash1 [link] [comments]  ( 1 min )

Hi, we submitted to ICML for the first time. We got 4 reviews and 3 of them are mostly positive. Major comments by the reviewers include: more justification on the assumptions, discussion on choices of parameters, and experiments in more complex and different environments. We want to address all the major and minor comments as best as we can but given that the response is limited to one page we cannot explain everything in detail. I am not sure what is the acceptable norm here. Do reviewers expect the authors to conduct some experiments during the rebuttal and provide sample results or just explain what additional experiment we will conduct and how we will do it. Justification and reasoning should be in details or a brief explanation with an assurance to add a detailed discussion in the final version suffices. TIA submitted by /u/srvsinha186 [link] [comments]  ( 1 min )

Hey all, I have a technical program manager interview soon for an ML team at google and I want to know if anyone has any sample role-related questions I can gauge myself with. I have a strong data science & statistics background but that doesn't always translate to deep ML knowledge like an ML Engineer might have. Any resources or sample questions? I have not found adequate results from google regarding this team area specifically. submitted by /u/math_is_my_religion [link] [comments]  ( 1 min )

Hi everyone. This is my first-time post here, and I hope I did not break any sub rules. Currently, I am doing some research with the UCI Adult dataset(https://archive.ics.uci.edu/ml/datasets/adult). This first step is to build a high-accuracy classifier model. Does anyone know any high accuracy model on this dataset (more than 90%)? I use many machine learning models like logistic regression and neural network. But no matter how complex the model is, I can only get an accuracy of about 85% on the test set. I tried to google but I found many others also have similar results of about 85%. Any posts or papers will be helpful! Thanks in advance for your help! submitted by /u/Akasakura888 [link] [comments]  ( 1 min )

Predicting longtail events can be one of the more challenging ML tasks. Last year my team published a blog article where we improved DoorDash’s ETA predictions by 10% by tweaking the loss function with historical and real-time features. I thought members of the community would be interested in learning how we improved the model even more by using Gamma distribution-based inverse sampling approach to loss function tuning. Please check out the new article for all the technical details and let us know your feedback on our approach. ​ https://doordash.engineering/2022/04/06/using-gamma-distribution-to-improve-long-tail-event-predictions/ submitted by /u/pmp-dash1 [link] [comments]  ( 1 min )

Hey there, just a heads up we at The Gradient just published a new article discussing explainability - "This article uses the common backdrop of competitive games to explore the ways in which domain experts adapt to new technologies that lack explainability. I illustrate how interpretations vary based on user experience and model architecture, and how special care must be taken when adapting models to human-centric problems." Check it out here if you think it's interesting / worth discussing: Reading the Tea Leaves: Expert End-Users Explaining the Unexplainable submitted by /u/regalalgorithm [link] [comments]  ( 1 min )

Hi all, I just wanted to share a project I've been working on for the past year - using deep RL to learn to play the board game Settlers of Catan. I expect everyone is aware of the results that DeepMind/OpenAI have got recently on Go, DOTA 2, Starcraft 2 etc, but I was motivated to see how much progress could be made with existing RL techniques on a reasonably complex game - but with access to significantly less computational resources. Whilst I didn't end up with an agent that performs at a super-human level, there was clear learning progress and the results were quite interesting. I decided to do a full write-up of the project here, which I figured could be useful for anyone else who is interested in trying to apply DRL to a new, complicated environment. I also open-sourced all the code here for anyone interested. If anyone has any feedback or any questions at all that'd be great! submitted by /u/henrythepaw [link] [comments]  ( 3 min )

Hi, We just submitted to ICML 2022 and got our reviews back. We were excited to see that 4/4 reviews were positive and acknowledged the contribution of the paper. However, there were some minor criticisms (e.g. didn't do good enough lit reviews, could use a few more experiments) across several reviews. I was wondering if it is ever acceptable to not submit a rebuttal? Can a rebuttal in this case actually hurt us by rocking the boat---or for ICML is the norm that you should always submit a rebuttal that addresses all the reviewers' criticisms. We were wondering what the norm is for ICML specifically? submitted by /u/optimistic313 [link] [comments]  ( 1 min )

Blog post. Paper (pdf file format). The paper is also linked to in the above blog post. Abstract Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style. To leverage these representations for image generation, we propose a two-stage model: a prior that generates a CLIP image embedding given a text caption, and a decoder that generates an image conditioned on the image embedding. We show that explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity. Our decoders conditioned on image representations can also produce variations of an image that preserve both its semantics and style, while varying the non-essential details absent from the image representation. We use diffusion models for the decoder and experiment with both autoregressive and diffusion models for the prior, finding that the latter are computationally more efficient and produce higher-quality samples. OpenAI's Sam Altman used DALL-E 2 to generate ~20 text prompt requests from Twitter users. The results are here, with individual result links and other samples in this comment from another Reddit user in a different post. Twitter thread about the paper (not from the paper authors). Sam Altman's blog post about DALL-E 2. Hopefully this summer, we’ll do a product launch and people will be able to use it for all sorts of things. submitted by /u/Wiskkey [link] [comments]  ( 3 min )

I'm curious about wether this unusual learning trajectory observed in humans has also been observed in artificial neural nets. A well known phenomenon in the 'dark souls' video game series is that ones first attempt at a boss is often much better than subsequent attempts. Boss hp at time of death by attempt might go something like: 35%, 55%, 85%, 87%, 75%, 54%, , 60%, 43%, 27%, 38%, 12%, 0%. This sounds very anecdotal, but its know to the community of these games to be a real thing. See this thread for evidence. Have NN playing games been known to exhibit a similar pattern, with peak in success early on, followed by a step descent , then a slow gradual climb? Or is this a purely human phenomenon? My hypothesis as to why this happens is that over the course of the first couple attempts, the player learns a bunch of bad strategies which must be slowly unlearned, whereas on attempt one, the player has no defined strategies good or bad. submitted by /u/Greenface1998 [link] [comments]  ( 3 min )

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Just a side project (only for me) in which I try to sum up some history of DL. Can't be 100% sure this is the first article in which they appear: Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2008). The graph neural network model. IEEE transactions on neural networks, 20(1), 61-80. Would appreciate any help. Thanks submitted by /u/Siddh__ [link] [comments]  ( 2 min )

(not my paper) paper: https://arxiv.org/abs/2204.02112 abstract: "The Bayesian additive regression trees (BART) model is an ensemble method extensively and successfully used in regression tasks due to its consistently strong predictive performance and its ability to quantify uncertainty. BART combines "weak" tree models through a set of shrinkage priors, whereby each tree explains a small portion of the variability in the data. However, the lack of smoothness and the absence of a covariance structure over the observations in standard BART can yield poor performance in cases where such assumptions would be necessary. We propose Gaussian processes Bayesian additive regression trees (GP-BART) as an extension of BART which assumes Gaussian process (GP) priors for the predictions of each terminal node among all trees. We illustrate our model on simulated and real data and compare its performance to traditional modelling approaches, outperforming them in many scenarios. An implementation of our method is available in the \textsf{R} package \texttt{rGPBART} available at: https://github.com/MateusMaiaDS/gpbart." submitted by /u/bikeskata [link] [comments]  ( 2 min )

I’m currently writing a research paper for my MSc on neuromorphic sensing and spike neural networks and most good papers are from around 2015 and was looking for something more recent. Anyone here heard of any interesting upgrades in architecture or applications? Cheers! submitted by /u/GandhisLittleHelper [link] [comments]

Hello, I’m currently a graduate student. I do different projects and for some I get to decide on what I want the scope to be. I do have to get the scope/ plan/ idea approved first. I pitch my ideas to profs who aren’t directly my profs and normally 5-6 other students will pitch ideas to the same group of profs at the same time….. I noticed that i get really different questions and feedback in comparison to my peers. I’m a female and my peers are male… I didn’t start out with this outlook but I’m starting to search for reasons why I often get questioned about my capabilities to preform a project ( which is normal enough but I get questioned to the point where explaining my approach isn’t enough and they ask me for examples of codes) and my peers definitely do not get asked about there capabilities, rather they tell them what they can do and they don’t get questioned. …………… really frustrating. submitted by /u/tyger-lily [link] [comments]  ( 4 min )

As a project in the course of my PhD, I had to create a prototype for a project. My PhD is application of machine learning in health care. The project definition and scope was faaaar too wide. However, I managed to create a working demo which encompasses some use cases of the project. At best, it can be called a framework, where I have put in different DL components and it works okay for those use cases only. Most of the components, I have used are pre-trained language models (maybe fine tuned them to my use case). However, there is no active training or learning involved. This is because I created this for a demo only. I also created a very small dataset and tested the framework over the dataset and the results were ok. However, my supervisor now wants me to write a paper, as he is confident, that the use case is rather unique and my working framework is a good first step. I believe, his aim is to get me started on the paper writing process, which I appreciate. However, I am not confident about it at all. My question is, can a 'framework' composed of pre-trained models with the end goal of solving a problem in health care is good enough? Are there precedents of any such paper? And if I trust my supervisor's instincts, are there any fancy ways to frame the framework so that it does not look so basic? submitted by /u/Complex_State9960 [link] [comments]  ( 2 min )

I am trying to build a knowledge based recommender system but do not have prior knowledge. We first take in user inputs such as occasion, weather, top wear and bottom wear, color. Based on this we want to create a knowledge base and recommend clothes. Can anyone help me on how to go about on doing this process step by step and what algorithms and technology to be used? submitted by /u/bills70 [link] [comments]  ( 1 min )

ICML 2022 paper reviews are supposed to be released soon. Creating a discussion thread for this year's reviews. submitted by /u/zy415 [link] [comments]  ( 2 min )

I’ll give an example to better explain my question (don’t get hung up on the numbers, it’s all made up). Say you are using a tree based model trying to project how many points a player will score in a given basketball game. Most players shoot free throws at a slightly lower percentage on the road, than they do at home. However, the magnitude varies player to player. Let’s assume for 95% of players with significant data, the ratio of home free throw percentage to away is 1 to 1.15. Generally speaking, older players are closer to 1 and younger players are around 1.1 (since older players get used to the opposing crowd). Now also say it takes 100 home and 100 away free throws to get a stable reliable ratio. Now say a young player only has 50 home, and 50 away free throws. With this amount of data he has a ratio of 1, however the sample size is not enough to be fully stable. Which would be better… 5 features into this model, his home away ratio, average ratio for players his age, home free throw count, and away free throw attempts. 1 feature. His ‘projected’ home away ratio, which is a weighted average of his ratio with the average for plaeyrs his age. Since he’s 50% of the way to significance, 0.5 * 1 + 0.5 * 1.1 = 1.05 The benefit of the of the first choice is that it may find other interactions that I never conceived of, however, it could incorporate noise. Is there a general consensus, or is this just a try both and see what works? submitted by /u/irndk10 [link] [comments]  ( 4 min )

I tried posting this on stackoverflow with no response. Im trying to use model.save() and keras.models.load_model() on this chunk of code. But, unlike some of the other keras examples I've played with, this one seems to crash. I'm super new to this, any Ideas why? I can post the error message if it helps. submitted by /u/HoneyBunchsOGoats [link] [comments]  ( 1 min )

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Meet the electric vehicle that’s quick-witted and fully outfitted. Last week, NIO began deliveries of its highly anticipated ET7 fully electric vehicle, in Hefei, China. The full-size luxury sedan is the first production vehicle built on the NIO Adam supercomputer, powered by four NVIDIA DRIVE Orin systems-on-a-chip (SoCs). The production launch of its flagship sedan Read article > The post Fast and Luxurious: The Intelligent NIO ET7 EV Built on NVIDIA DRIVE Orin Arrives appeared first on NVIDIA Blog.  ( 2 min )

In its debut in the industry MLPerf benchmarks, NVIDIA Orin, a low-power system-on-chip based on the NVIDIA Ampere architecture, set new records in AI inference, raising the bar in per-accelerator performance at the edge. Overall, NVIDIA with its partners continued to show the highest performance and broadest ecosystem for running all machine-learning workloads and scenarios Read article > The post NVIDIA Orin Leaps Ahead in Edge AI, Boosting Leadership in MLPerf Tests appeared first on NVIDIA Blog.  ( 3 min )

Amazon Rekognition Custom Labels is a fully managed computer vision service that allows developers to build custom models to classify and identify objects in images that are specific and unique to your business. Rekognition Custom Labels doesn’t require you to have any prior computer vision expertise. You can get started by simply uploading tens of […]  ( 7 min )

When artificial intelligence is tasked with visually identifying objects and faces, it assigns specific components of its network to face recognition — just like the human brain.  ( 5 min )

A new technique compares the reasoning of a machine-learning model to that of a human, so the user can see patterns in the model’s behavior.  ( 7 min )

For nine years, my family and I have lived in a house in Issaquah, a little community about twenty minutes east of Seattle. The town still retains its charms — a downtown area about three blocks long that includes a vintage (and long since decommissioned) gas station, numerous restaurants, a live theater, the library, and… Read More »DSC Weekly Digest: Moving Time The post DSC Weekly Digest: Moving Time appeared first on Data Science Central.  ( 7 min )

Posted by Ikechukwu Uchendu, AI Resident and Ted Xiao, Software Engineer, Robotics at Google Reinforcement learning (RL) can be used to train a policy to perform a task via trial and error, but a major challenge in RL is learning policies from scratch in environments with hard exploration challenges. For example, consider the setting depicted in the door-binary-v0 environment from the adroit manipulation suite, where an RL agent must control a hand in 3D space to open a door placed in front of it. An RL agent must control a hand in 3D space to open a door placed in front of it. The agent receives a reward signal only when the door is completely open. Since the agent receives no intermediary rewards, it cannot measure how close it is to completing the task, and so must explore …  ( 8 min )

DALL·E 2 is a new AI system that can create realistic images and art from a description in natural language.  ( 2 min )

A couple days ago I wrote about how Vieta’s formulas let you sum the zeros of a polynomial without having to first compute the zeros. This is especially handy for high-order polynomials since there is no explicit formula for the zeros. Most functions that arise in applications are not polynomials. How could you find the […] Sum the zeros of an analytic function without finding them first first appeared on John D. Cook.  ( 3 min )

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We humans navigate the environment using all of our senses. Allen Institute researchers propose MERLOT Reserve, a model that learns to represent videos over time and across several modalities, including audio, subtitles, and video frames. It was trained using a new learning objective and more than 20 million YouTube videos. MERLOT Reserve is a unique, cutting-edge methodology for solving video-related inquiries. MERLOT Reserve can dependably choose the correct answer from a selection of multiple-choice answers when given a video and a question. This forecast is made by MERLOT Reserve jointly reasoning over the visual frames of the video, the video subtitles, and the audio in the movie. Continue reading this cool research update from AI2 Paper: https://arxiv.org/pdf/2201.02639.pdf Demo: https://merlot-reserve.apps.allenai.org/ Project: https://rowanzellers.com/merlotreserve/ Github: https://github.com/rowanz/merlot\_reserve ​ https://preview.redd.it/031i6ty6err81.png?width=1920&format=png&auto=webp&s=299569e12160eb991f35a2c6b41c5758ff027235 submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

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Hi, all. I got accepted into my top choice of college as an undecided major. Recently, I have decided to pursue artificial intelligence! Unfortunately, it is near impossible to transfer into computer science at my particular university. I was wondering if I can still pursue AI as a career if I complete one of the following majors: -Mathematics -Information or Data Science -Statistics -Linguistics Additionally, I could pursue one of these and minor in another. I should be able to minor in computer science as well if necessary. Hopefully, my choice of major would allow me to pursue research or an internship in artificial intelligence. I am willing to take additional summer courses and pursue relevant certifications to ensure that I am up to par with my computer science colleagues. (posted on behalf of a family member) submitted by /u/runelagoon [link] [comments]  ( 2 min )

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I upscaled an image with a few different superres models and programs, pick your favorite! https://files.botbox.dev/superrestestcollage.png Because of how reddit is, I can't make this as a poll, so comment your pick. Animated original version: https://www.youtube.com/watch?v=zRaTwVuqd70 (I will also make an animated version upscaled with the most voted model/program) submitted by /u/Recent_Coffee_2551 [link] [comments]

I am looking for something to change my voice in a way that is more satisfactory and more convincingly varied than what simple voice modulation software can achieve and as cheaply as is possible (preferably free). Use case: I have been working on an animated movie to which I am the sole contributor. Though I have been putting it off while looking for an appropriate solution, the time has come to voice my various characters, who are a range of ages, both male and female. For several reasons, I am interested in voicing them all myself while doing the facial motion captures as well. What I am in need of is, essentially, something that does exactly what Respeecher does, but without the $200/month sub fee. I would love to be in a position to simply pay them what they are asking for in exchange…  ( 2 min )

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I have been searching for a music AI that takes input as mp3 or midi files, yet haven't been successful yet. Is there such a thing? If not, is such a thing feasible? submitted by /u/16pxl [link] [comments]  ( 1 min )

Perceiver and PerceiverIO (https://arxiv.org/abs/2107.14795) appear to offer significantly improved FLOP efficiency, but new LLMs (including Deepmind's own Gopher) don't use it. What gives? Is it still too new, or is the Perceiver architecture not appropriate for LLMs? submitted by /u/deeceeo [link] [comments]

Saw this posted on Schmidhuber's Twitter: Meta-Learning Machines in a Single Lifelong Trial: lecture video (24 min) presented at meta-learning workshops at ICML 2020 and NeurIPS 2021. URL of talk: https://youtu.be/2GgGVdkq2bU Abstract The most widely used machine learning algorithms were designed by humans and thus are hindered by our cognitive biases and limitations. Can we also construct meta-learning algorithms that can learn better learning algorithms so that our self-improving AIs have no limits other than those inherited from computability and physics? This question has been a main driver of my research since I wrote a thesis on it in 1987. In the past decade, it has become a driver of many other people's research as well. Here I summarize our work starting in 1994 on meta-reinforcement learning with self-modifying policies in a single lifelong trial, and - since 2003 - mathematically optimal meta-learning through the self-referential Gödel Machine. This talk was previously presented at meta-learning workshops at ICML 2020 and NeurIPS 2021. Many additional publications on meta-learning can be found at https://people.idsia.ch/~juergen/metalearning.html submitted by /u/hardmaru [link] [comments]  ( 1 min )

Hi *, I've been working for the last 5 years as Data Scientist. During this time I have tried dozens of times to improve my models via hyperparameter tuning, but I've never got improvements from there. I've tried all the possible approaches: grid search, random search, bayesian search, etc. But in no case did I get satisfactory results. Does this happen to anyone else? Have you ever got robust improvements via HP tuning? submitted by /u/AM_DS [link] [comments]  ( 1 min )

Autoregressive models like GPT-2 do fairly well in text generation. Is it possible to do the same for graph data? A transformer based model Graphormer has recently shown its effectiveness in graph representation learning. Is there any way I can train Graphormer or any other model to generate graphs from an initial graph context? submitted by /u/ratt_m [link] [comments]

Hi! I'm a first-year Grad Student in Computer Vision and I am trying to get caught up on the latest research in my field. It seems like everyone in CS has heard about all of the latest papers but I just have no idea how. My knowledge is limited to general ideas and doesn't know any specific papers unless they have like 20000+ citations. So my question is: how do you hear about these papers and get caught up? Is there a reference somewhere that puts together a list of all the "must-read" papers that have come out? I feel like I am already 5 years behind in my knowledge. It would be great if there was something like "Top 5 papers of the week" that I could read to stay on top of things. Also, this doesn't just apply to Vision. I would like to have an idea of the other major developments in other fields (like NLP, general ML/DL, etc.) since I think that can carry over to my field. Thanks! Looking forward to your replies submitted by /u/TobusFire [link] [comments]  ( 2 min )

Based on my experiences in both academia and industry, I see that many researchers get listed as authors on papers solely for having attended the relevant project meetings, despite not contributing anything substantial to the work. I know of several people who've gotten on dozens of papers this way, despite not being able to explain the main details behind many of the papers they "co-authored." Of course, they can then claim credit for the work publicly as well as have their academic profile benefit from the citations accrued by the work. I've noticed that typically, these people are initially invited onto the project because they are on chummy terms with someone on the project. Concerningly, the more someone successfully "paper-rides" this way, the stronger their publication record looks, which makes it easier for them to find their way onto more projects to paper ride in the future. It seems that the obsessive focus on paper counts and citations has encouraged the rise of intellectually dishonest strategies for maximizing one's academic footprint. The huge research scientist salaries at top industry labs, which similarly obsess over paper counts and citations in their hiring process, only amplifies the incentive for paper riding. The reason I think it is bad: As more people paper ride, co-authorship on a paper gradually becomes a worse indication of expertise. Not to mention, paper riders are intellectually dishonest, by claiming credit for research that they didn't significantly contribute to. In a sense, it seems like a roundabout form of plagiarism. I know some might disagree with this take, as some people believe in being as generous about co-authorship as possible. I find that mindset to create the perfect environment for paper riders to flourish. I'm wondering if you've also seen paper riding happen and whether you think this behavior is good or bad. submitted by /u/alwayshumming [link] [comments]  ( 7 min )

Hi there experts, I have a real distribution for which I had run this scipy script to detect the best fit: However, the script outputs 4 parameter values and the best fit is actually a Exponentiated Weibull distribution. Now I am clueless how to generate a sample list of data of n-size. I know for sure about the normal distribution after getting these params as mean and sigma. How to I generate such list. Please help. ​ ​ https://preview.redd.it/79n28icmsqr81.png?width=1141&format=png&auto=webp&s=d9478691c06f5cdfe03af4f82db8293443e91f1e submitted by /u/GoldenDew9 [link] [comments]  ( 1 min )

I understand that certain AE types such as B-VAE disentangle certain aspects of variation in the data, and those such as Conditional AE or VAE allow us to separate these aspects with labels. However, what I have seen is that the embedding space doesn't cluster the images as well as some contrastive methods. However contrastive methods require non-elegant negative sampling etc. Can we somehow force the VAE to learn both the variational lower bound as well as learn a good metric between samples such as visually similar samples are better clustered together? submitted by /u/jim_from_truckistan [link] [comments]  ( 1 min )

The Jetson Orin 64gb model has "275 Sparse|138 Dense INT8 TOPS", and I am a little confused about how to compare this to something like the RTX a6000's performance. I am looking to do deep rl training and am new to the field. What metrics make a difference for deep rl? Any thoughts on the Orin dev kit's ability to train deep rl? submitted by /u/here_to_create [link] [comments]  ( 1 min )

Some time down the road, when AutoML becomes more established, it can help us determine the best ML model and hyperparameters for a particular problem. This will not replace data scientist, as we still need data scientists for their domain knowledge, which is critical for scoping business problems, pre-processing data, and deriving business insights from the trained model. However, since data scientists no longer need to deal with the technicalities of a model in the near future (i.e. they no longer have to tune hyperparameters, determine the best opitmistion function etc), is there still a need for aspiring data scientists to learn about the intricacies and nuances behind the various models (maybe by coding the model from scratch)? Or is it enough for them to learn how to operate an AutoML system? (My question is referring to the corporate world in general and not to academia) Thanks in advance for your answers :) submitted by /u/smart_oinker [link] [comments]  ( 7 min )

Hi everyone! We've just launched a competition at the AutoML-Conf 2022, the DAC4AutoML competition. It has two tracks, one for configuring a Computer Vision model and one for a RL pipeline: https://automl.github.io/dac4automlcomp/ And what is DAC exactly? It means we want to find well-performing hyperparameter configurations like in Algorithm Configuration, but we do it dynamically - thus DAC, Dynamic Algorithm Configuration. As to how that is supposed to happen? We don't put any restrictions on the solutions for the competitions, so you can submit your hand-tuned static hyperparameter setting if you want. Or you can use some sort of heuristic, a regression model, reinforcement learning, ... whatever works. If you're interested in participating, you can submit from now on until the 18.06. AOE, the winners will be announced at the AutoML-Conf. submitted by /u/catsortion [link] [comments]  ( 1 min )

As I understood it Imagenet got generated from internet images, but I am unable to to find the originals using naive image search. Is there any mapping? I wonder if imagenet data is a cropped versions of original pictures or not, i don't see it in the paper. submitted by /u/LeanderKu [link] [comments]  ( 1 min )

Article describing depth estimation using machine learning models and 3D visualization of depth maps using three.js. https://www.storminthecastle.com/posts/ufos_and_depth/ submitted by /u/CakeStandard3577 [link] [comments]

In the context of text-to-image generation, I'd say one of the reasons VQGAN is so used in popular notebooks is that it can deal with many concepts, while stylegan used to be limited to the domain it was trained for. That may be about to change with the rollout release of Stylegan-XL weights trained on Imagenet. This notebook (https://github.com/CasualGANPapers/StyleGANXL-CLIP) has had nice results with objects never seen by the model, such as "apple" and "ant", as well as scenes such as "judo athletes fighting" Please note that the Stylegan-XL weights are currently available for 128x128 pixels. ETA for the 256 resolution is 14.04.22 submitted by /u/HrodRuck [link] [comments]  ( 1 min )

My post is inspired by this discussion. In that thread, OP asked why support vector machines are still taught. People offered several thoughts: they're easier to think about, they're still perfectly good for some real-world problems, and for some problems they apparently rival deep networks. I did a project for a class around six years ago using an SVM as implemented in scikit-learn. I was pretty satisfied with the project, but I also experienced some frustrations, and came away with some questions. I started working with Tensorflow and DNNs in earnest soon after finishing that project, and I largely stopped thinking about SVM. I would like to revive the questions I asked, but never answered, here. A DNN with multiple outputs can potentially use a single neuron in the prediction of more than one output. For multiple, mutually-exclusive categories, this makes good sense. An SVM with multiple outputs in scikit-learn was implemented as pairs of one-vs-one SVMs, each of which was independently fit to data. This gets inefficient quickly. Has this changed? Can it be changed? DNN training at scale is a problem that many people have worked hard to make practical. Even non-experts like myself use our home GPUs to accelerate training of DNNs on large data sets. In scikit-learn, SVM training was implemented in a single thread on one CPU core. If you are performing cross-validation or a hyperparameter optimization study, it might be practical to parallelize fitting; one thread for each distinct condition. But can you parallelize the SVM fitting algorithm for a single condition? I went looking for software, but I couldn't find anything. Over to you folks. Cheers. submitted by /u/aotus_trivirgatus [link] [comments]  ( 6 min )

​ Visual Results With Restormer, you can remove noise, motion blur, defocus blur, and rain streaks from your own images. Paper: https://arxiv.org/abs/2111.09881 Github: https://github.com/swz30/Restormer Colab Demo: https://colab.research.google.com/drive/1C2818h7KnjNv4R1sabe14_AYL7lWhmu6?usp=sharing Gradio Web Demo: https://huggingface.co/spaces/swzamir/Restormer submitted by /u/swz30 [link] [comments]  ( 1 min )

Does anyone have any advices or research papers on what hyperparameters do researchers use for their seq2seq model? I am interested in knowing whether hyperparameters such as dropout, or recurrent dropout, batchnorm, etc etc, are even necessary in the usage of seq2seq model, but couldn’t find anything on it for weeks. In the case, let’s say, using gridsearchCV, what hyperparameters do you tweak for ur seq2seq model? (Other than the usual stuff like number of neurons, etc). There is absolutely zero information for that on seq2seq model, and everyone just assumes that putting an attention mechanism solves everything without hyperparameters tunings. I have also looked up on codes on seq2seq, and no hyperparameters tunings were shown whatsoever. FYI, this is in the context of time series data, using seq2seq, if that matters. Thanks submitted by /u/plsendfast [link] [comments]  ( 1 min )

I’ve been reading many papers lately pertaining to GANs, with more and more introducing supervised loss into the generator’s objective function. However, no one ever seems to show that the optimum remains undisturbed. Results seem to be strictly empirical most of the time. Has anyone seen any papers where it is shown that the disruption to the generator’s loss doesn’t harm convergence? submitted by /u/king_of_walrus [link] [comments]  ( 1 min )

I am curious what is everyones experience with completely faked, falsified, or fabricated results in the area? Another aspect of this I think is people taking heavily overfitted results and finding one decent example that is from the test set and claiming their method is awesome. How much of this have you seen and how much of the research out there fits into this category? submitted by /u/LifeguardDismal142 [link] [comments]  ( 1 min )

Sponsored Post Join the UK’s most forward-thinking technologists and business professionals this June in a celebration of emerging technology. Machine […] The post Win tickets to The AI Summit London 2022 appeared first on Machine Learning Mastery.  ( 2 min )

submitted by /u/aidev2040 [link] [comments]

When I was the Vice President of Advertiser Analytics at Yahoo!, I painfully learned that my targeted user personas (Media Planners & Buyers and Campaign Managers) didn’t want more data in helping them optimize their marketing, campaign, and advertising spend across the Yahoo! Ad Network.  Heck, they didn’t even want analytics!  The aspirations for these… Read More »Building a Data Products-centric Business Model The post Building a Data Products-centric Business Model appeared first on Data Science Central.  ( 5 min )

Real-world production ML systems consist of two main components: data and code. Data is clearly the leader, and rapidly taking center stage. Data defines the quality of almost any ML-based product, more so than code or any other aspect. In Feature Store as a Foundation for Machine Learning, we have discussed how feature stores are… Read More »Data Discovery for ML Engineers The post Data Discovery for ML Engineers appeared first on Data Science Central.  ( 9 min )

Many things can help you to write good dissertations. One of the most important is to use content metrics. It is necessary for all of the students to understand content metrics in detail. A clear understanding of its types and measuring strategies help you to evaluate things in a precise way. Whatever is your topic… Read More »Content Metrics That Can Help You To Write Dissertations The post Content Metrics That Can Help You To Write Dissertations appeared first on Data Science Central.  ( 5 min )

The need of a highly functional and fast processing Central Processing Unit (CPU) in today’s world is not just mostly desired, but also mostly required due to the rapid digitalization across the globe. Whether you work on a personal computer (PC) unit or laptop, the necessity of a highly advanced processor is indispensable.  This is… Read More »Comparative analysis of an Intel and AMD Processor The post Comparative analysis of an Intel and AMD Processor appeared first on Data Science Central.  ( 3 min )

How does artificial intelligence Diversity, Equity, and Inclusion (DEI) fit into the technological stack of daily companies? Fostering a diverse workforce is a very human problem. The cry for a halt to race prejudice has become deafening, and it’s increasingly a decisive factor for talent when weighing job offers and purchases. To stay up with the… Read More »AI And Its Impact On Diversity And Inclusion The post AI And Its Impact On Diversity And Inclusion appeared first on Data Science Central.  ( 4 min )

Understanding consumer behavior is becoming more and more critical as businesses seek to find innovative ways to survive and thrive in a period of constant change. In the last few years, the market has seen significant changes in the way people shop, travel, dine and purchase goods. As a business, when it comes to understanding… Read More »How Data Intelligence Platforms Promote Business Success The post How Data Intelligence Platforms Promote Business Success appeared first on Data Science Central.  ( 4 min )

I read an article from the world economic forum which proposed an AI labeling system for AI products designed for children Today, for the first time, children are growing up in a world shaped by artificial intelligence (AI) and decisions are being made for children implicitly by AI.  Algorithms need data that is collected and… Read More »Exploring AI labeling for children’s products The post Exploring AI labeling for children’s products appeared first on Data Science Central.  ( 3 min )

I’ve been running circles in tutorial purgatory and I want to get out of it with sone projects. Anyone has any suggestions? Guided ones would be nice. For unguided ones, could you please provide source links/hints? submitted by /u/HellVollhart [link] [comments]  ( 1 min )

Hi all. I've been stuck on this problem for a while and I thought I might be able to find some help here. Any kind of assistance would be greatly appreciated. My setup is as follows. I have an environment with 3 agents. All 3 agents have a single policy network, and it is based on CommNet. My goal is to implement a replay buffer for this environment. I verified that my replay buffer logic is good. I tried running 3 different types of runs: Normal on-policy run: The agents perform an episode, and at the end of each episode the data (such as the states, actions, etc) from this episode are used to calculate the loss Using just the last episode from the replay buffer: The agents perform an episode, and the data is stored in the replay buffer. At the end of each episode, the last episode is sampled from the replay buffer (which is the episode that was just performed). This is just to confirm that my replay buffer is working properly, and the reward curve for this case matches that from (1). Using 1 random episode from the replay buffer: The agents perform an episode, and the data is stored in the replay buffer. At the end of each episode, a random episode is sampled from the replay buffer and used to calculate the loss. The performance is terrible in this case, and the environment times out each time For some reason, as soon as I turn on random sampling, progress is really bad. I'm sorry to pose such an open-ended question, but what are some things I could check to pinpoint the source of this problem? What could be a reason as to why performance is as expected when just sampling the last episode, whereas it is terrible when randomly sampling episodes? I've tried some things thus far but nothing has worked, and I turned to this community in hopes of getting some help. I'm new to the area of reinforcement learning, so I would be very grateful for any kind of help you can offer. Thanks in advance submitted by /u/lebr0n99 [link] [comments]  ( 3 min )

Hi, I'm wondering if the PPO algorithm can solve the sample correlation problem of on-policy algorithm in training. PPO uses successive samples to compute GAE, doesn't the sample correlation occurring here interfere with learning? submitted by /u/noisemastar [link] [comments]

submitted by /u/catsortion [link] [comments]  ( 1 min )

submitted by /u/bendee983 [link] [comments]

​ Output from using model.learn(env) on both Envs On the left I have a simple dummy CustomEnv (Using Stable-Baselines3 with Gym) for testing, and on the right I have my actual CustomEnv that I am working on in a project. As you can see, the dummy environment gives me the rollout monitoring, whereas there is no rollout monitoring for the actual environment (just time + train statistics/monitoring). I am using very similar code when setting up the training of the model, however the complexity of the actual model is significantly higher than the dummy. In theory, the complexity of the environment shouldnt make a big difference to the monitoring right? All of the key parts are still there (reward function, step function, reset function etc.). In both cases it says that the environments are being wrapped by the 'Moniter' wrapper so that cant be it. Does anyone know why this might be happening? submitted by /u/C_BearHill [link] [comments]  ( 1 min )

I’m working on Q table learning model for OpenAI’s. I have everything done in regards to a basic agent, but I’m unsure how I’m supposed to use the box data for action space and observance space, to populate a q table? Or is this approach incorrect? Car Racing doesn’t have a P (probability) call so I’m not sure how else I would do value iteration. submitted by /u/Dzartovian94 [link] [comments]  ( 1 min )

Hey guys, I am new to RL and walking through the Keras implementation of Actor Critic. ​ As a variant of it, I am trying to learn the strategy for WORDLE. However, after a few runs, my action spaces all go down to zero. Not sure what's happening. Could someone have any insights or pointers? ​ Attaching my code for reference. ​ Thanks import pandas as pd import numpy as np import random import string import random import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers # Configuration parameters for the whole setup gamma = 0.9 # Discount factor for past rewards max_runs = 10000 eps = np.finfo(np.float32).eps.item() # Smallest number such that 1.0 + eps != 1.0 my_file = open("", "r") content = my_file.read() content = lis…  ( 3 min )

In case my NeurIPS submission rejected, lol. submitted by /u/Blasphemer666 [link] [comments]  ( 1 min )

MIT researchers design a robot that has a trick or two up its sleeve.  ( 6 min )

Posted by Gil Shamir and Dong Lin, Research Software Engineers, Google Research Ever queried a recommender system and found that the same search only a few moments later or on a different device yields very different results? This is not uncommon and can be frustrating if a person is looking for something specific. As a designer of such a system, it is also not uncommon for the metrics measured to change from design and testing to deployment, bringing into question the utility of the experimental testing phase. Some level of such irreproducibility can be expected as the world changes and new models are deployed. However, this also happens regularly as requests hit duplicates of the same model or models are being refreshed. Lack of replicability, where researchers are unable to reproduce…  ( 9 min )

The built-in Amazon SageMaker XGBoost algorithm provides a managed container to run the popular XGBoost machine learning (ML) framework, with added convenience of supporting advanced training or inference features like distributed training, dataset sharding for large-scale datasets, A/B model testing, or multi-model inference endpoints. You can also extend this powerful algorithm to accommodate different requirements. […]  ( 5 min )

Research over the past few years has shown that machine learning (ML) models are vulnerable to adversarial inputs, where an adversary can craft inputs to strategically alter the model’s output (in image classification, speech recognition, or fraud detection). For example, imagine you have deployed a model that identifies your employees based on images of their […]  ( 12 min )

Square/Enix presents the fictional city of Midgar in Final Fantasy VII Remake at a filmic level of detail. Epic’s Fortnite bathes its environments in ray-traced sunlight, simulating how light bounces in the real world. And artists at Lucasfilm revolutionized virtual production techniques in The Mandalorian, using synchronized NVIDIA RTX GPUs to drive pixels on LED Read article > The post Unreal Engine and NVIDIA: From One Generation to the Next appeared first on NVIDIA Blog.  ( 4 min )

A dozen companies today received NVIDIA’s highest award for partners, recognizing their impact on AI education and adoption across such industries as education, federal, healthcare and technology. The winners of the 2021 NPN Americas Partner of the Year Awards have created a profound impact on AI by helping customers meet the demands of recommender systems, Read article > The post Green Teams Achieve the Dream: NVIDIA Announces NPN Americas Partners of the Year appeared first on NVIDIA Blog.  ( 4 min )

The previous post looked at the problem of finding the zeros of a cubic polynomial. Assuming we’re going to use a numerical method to calculate the zero, the hard part is knowing where to tell the numerical method to look. That post showed how to use a change of variables to guarantee that the polynomial […] Bounding zeros of an analytic function first appeared on John D. Cook.  ( 2 min )

The analog of the quadratic formula for cubic equations is cumbersome. A lot of people naturally say “Forget all that. If I need to find the roots of a cubic, I’ll just use a numerical method like Newton’s method.” Sounds good. Where to start? But how do you know where to look for the roots? […] Numerically finding roots of a cubic first appeared on John D. Cook.  ( 3 min )

The following is a slightly edited version of a Twitter thread on @AlgebraFact. The lowest C on a piano is called C1 in scientific pitch notation. The C one octave up is C2 and so forth. Middle C is C4. The frequency of Cn is approximately 2n+4 Hz. This would be exact if C0 were […] Mathematics and piano tuning first appeared on John D. Cook.  ( 2 min )

Once in a while it’s necessary to calculate some function of the roots of a polynomial, and it may be possible to do this without first calculating the roots. Quadratics The quadratic formula gives explicit solutions to the equation The two solutions for x are where The awkward part is taking the square root of […] Computing functions of roots without computing roots first appeared on John D. Cook.  ( 3 min )

This post contains a derives a result I needed recently. The derivation is simple but a little tedious, so I wanted to save it in case I need it again. Full width half maximum A common way to measure the width of a function peak in a function f(x) is to find the place x0 […] FWHM for a quadratic first appeared on John D. Cook.  ( 2 min )

Here’s a list of five numbers used as slang in various contexts. Location (CB and police radio) End of column (journalism) Best wishes (ham radio) All aircraft in area (US Navy) I love you (text messages) The motivation for this post was an article Those HTML attributes you never use. I wanted to make a […] Number slang and numbered lists first appeared on John D. Cook.  ( 1 min )

As I read the paper and several tutorials, I am quite confused about the details. I see many implementations scale the running cumulative discounted reward in a certain way. However, each of them does it in a different way. let R be the running cumulative discounted reward, which is considered the best? Or is there a source of the method to use? implementations I saw from different places include: directly use R to calculate the advantage and training value network (most PPO tutorials use this) use (R / std(R)), where std is the mini-batch standard deviation use (R / std(R)), where std is the running standard deviation use ((R - mean(R)) / std(R)), where both mean and std are mini-batch wise use ((R - mean(R)) / std(R)), where both mean and std are running stats. do the above and clip to a certain range ([-10, 10] or [-1, 1]) I also see several different ways for the value network, let V be the output of the value network: output raw logit, without any scaling/output activation (most PPO tutorials use this) output raw logit, but use the same scaling as discussed above for running cumulative discounted reward, for example, if return value is (R / std(R)), value output will be (V / std(R)) do the same as 2, but use the stat of V instead of R for scaling, for example, if the return value is (R / std(R)), the value output will be (V / std(V)) output with tanh activation at the last layer output with tanh activation at the last layer, and multiply by a constant to match the range of the return any help would be appreciated, thanks! submitted by /u/seermer [link] [comments]  ( 2 min )

Hello all, As the title describes, I’ll be making my first model as part of my final project. I still have a pretty high-level understanding of everything, so forgive any inaccuracies as I describe what I’m going for. The problem I’m attempting to solve is known as the traveling salesman problem. Essentially, a route needs to be formulated that stops at n given locations. Finding the most efficient route with many stops algorithmically is impractical because the number of possible routes increases exponentially with each added location. The environment will simulate travel on city roads. Speed will be a constant, set to whatever the roads speed limit is. I am using .pbf format vector GIS data from OSM so that the environment consists of real-world pathways. I’m using GeoPandas and Pyrosm to work with the data, and I’m collecting nodes for the location of gas stations so that the environment can simulate needing to fuel the vehicle. Gas price will be a constant, as well as vehicle fuel-efficiency. Scoring will be based on the calculated time it would take to complete a route and the calculated cost (in gas). The goal will be to find the most efficient route to take when n = some large number. I’ve never worked with spatial data either, so I’m not sure what kind of challenges that poses. I worry that adding nodes for the locations of gas stations might be difficult. I’m also wondering if I’m better off using Tensorflow and Keras for this, but I’m not really aware of all the technical considerations I should be making before deciding on that. Do you have any tips that might help me out? Solutions to problems I haven’t hit just yet, but likely will? Thanks for your help! submitted by /u/professorDissociate [link] [comments]  ( 2 min )

As far as I am aware, StableBaselines3 is the gold standard for reliable implementations of most popular / SOTA deep RL methods. However working with them in the past, I don't find them to be the most usable when looking for extensibility (making changes to the provided implementations) due to how the code base is structured in the behind the scenes (inheritance, lots of helper methods & utilities, etc.). For example, if I wish to change some portion of a method's training update with SB3 it would probably involve overloading a class method before initialization, making sure al the untouched portions of the original method are carried over, etc. Could anyone point me in the direction of any implementations that are more workable from the perspective of extensibility? Ideally implementations that are largely self contained to a single class / file, aren't heavily abstracted aware across multiple interfaces, don't rely heavily on utility functions, etc. submitted by /u/Farconion [link] [comments]  ( 1 min )

Given a NN class, is there something specific we need to care of when converting *args and **kwargs to a canonical kwarg representation? I ask this because in this code from Google (https://github.com/google-research/google-research/blob/c56b47713b08c95ad427d5f93ee0dbb9ad008964/social_rl/multiagent_tfagents/joint_attention/attention_networks.py#L557) they use a TFDecorator-aware replacement for inspect.getcallargs, instead of using getcallargs directly. So my questions are: - Is it possible to use inspect.getcallargs to convert *args and **kwargs to a canonical kwarg representation? - If no, is there an equivalent in PyTorch? I couldn't find any, so I was wondering how people go about that. submitted by /u/No_Possibility_7588 [link] [comments]  ( 1 min )

Hi all, I am running some starter code from openAI(FetchReach-v1, FetchPush-v1) gym with env.action_space.sample(). But I don't see the goal is actually achieved when done returned is True. I copied the code from here (https://openai.com/blog/ingredients-for-robotics-research/). I even let it sleep every step to watch more closely. Another related thing that I can't explain is that it always returns done==True rather quickly with very few sampled actions. These all make me worried about using it as my task environment. submitted by /u/AnimatorRemarkable20 [link] [comments]  ( 1 min )

Hello /r/reinforcementlearning. I have to choose electives pretty soon, and as i am interested in reinforcement learning, I wanted to know which of these would be the most beneficial. Monte Carlo Simulation Computational Learning Theory The year after I will also take a course on Reinforcement Learning, but it has not been created yet. Note: I can also take both if recommended, if I do so, I will take one of the courses before taking the RL course, and the other would be at the same time. Some further thought I've had: CLT includes Bandits, which is surely were useful to know, but it seems to be only a rather small part, and I'm unsure whether all the other topics like PAC Learning and Rademacher Bounds are useful? MC is more practical while CLT is more theoretic (Apparently VERY theoretic according to the course description above). I am not afraid of theoretic courses, but I struggle more with them than more practical courses. The sentiment around the MC course, is that it is pretty good. I don't know anyone who have taken the CLT course. If I choose both, which order would you take them in? submitted by /u/John_Hitler [link] [comments]  ( 2 min )

Hey i am using the RL lib from ray and i don't know if the observations automatically normalized by the lib or not? By creating a costum environment ray wants you to create an observationspace. That would be a gym box in my case. Anyway idk the exact high and low values. My values lay between -1 and 1 more or less. My fear is now that ray would normalize the Observation values to a new range although they are already processed. Does ray normalized observationspace? If yes how can i turn it off? Thanks! submitted by /u/Willing-Classroom735 [link] [comments]  ( 1 min )

CALL FOR PAPERS EvoRL 2022 Evolutionary Reinforcement Learning workshop at GECCO 2022, July 9-13, Boston, USA In recent years reinforcement learning (RL) has received a lot of attention thanks to its performance and ability to address complex tasks. At the same time, multiple recent papers, notably work from OpenAI, have shown that evolution strategies (ES) can be competitive with standard RL algorithms on some problems while being simpler and more scalable. Similar results were obtained by researchers from Uber, this time using a gradient-free genetic algorithm (GA) to train deep neural networks on complex control tasks. Moreover, recent research in the field of evolutionary algorithms (EA) has led to the development of algorithms like Novelty Search and Quality Diversity, capable of…  ( 2 min )

In the paper it is written to collect trajectories of length T. Then calculate advantage and then train the Actor and Critic Network. My question is suppose one episode ends much before T. If I run that episode upto lenth T then it will only collect negative rewards in each timestep which in turn makes the training impossible as the return if very big negative number. So what can be done instead of this? I might be getting it wrong, so please correct me by commenting. submitted by /u/Better-Ad8608 [link] [comments]  ( 1 min )

Hello, I'm making a custom openAI gym environment to train various algorithms on it. I have encountered some issues. My .flatten() method on the state class returns a large integer which can be converted back into the same state as the object. However when I try to do this as the returned observation for environment.reset() and environment.step(), when testing it I get: "AssertionError: The observation returned by the `reset()` method does not match the given observation space" which I can fix by having it just return a 0. How do I go about resolving this? and are there any better approaches for wanting to train RL agents on an environment? ty! submitted by /u/snaredrum_merchant [link] [comments]  ( 1 min )

Honest question: are there any applications for which SVMs are the best choice? In my experience, no one seems to use this methodology anymore, though maybe I'm wrong. It just kinda feels like teaching people how to use a slide rule when everyone has calculators. submitted by /u/WartimeHotTot [link] [comments]  ( 3 min )

https://youtu.be/zEMOX3Di2Tc This paper finds what seems to be a new phenomenon when working in the continual learning/life-long learning domain. If new tasks are continually introduced to an agent, it seems to loose it's ability to learn the more time progresses. Intuitively it's similar to this idea that "an old dog can't learn new tricks". They propose a fairly simple method of overcoming this limitation that involves resetting weights that are not contributing much to the outcome of the network. They call the method Continual Backprop. ​ Outline: 0:00 - Overview 2:00 - Paper Intro 2:53 - Problems & Environments 8:11 - Plasticity Decay Experiments 11:45 - Continual Backprop Explained 15:54 - Continual Backprop Experiments 22:00 - Extra Interesting Experiments 25:34 - Summary ​ Paper link: https://arxiv.org/abs/2108.06325 submitted by /u/SlickBlueML [link] [comments]  ( 1 min )

Blog: https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html Paper: https://goo.gle/palm-paper - AFAIK from the Blogpost, Scaling laws still hold up (i.e not yet plateaued) - New transfer learning capabilities, outperforms fine-tuned models with 50x less data (Codex-12B) - The interesting part is how it meta-learns techy geeky jokes and is able to correlate concepts, and explain jokes suggesting starting doing a bit more meta-learning than GPT3 ever could.... But still not enough to generate decent ones (though the joke wasn't particularly humorous, so I may be underestimating) SoTA on various tasks, chain-of-thought-reasoning still holds up to scaling and outperforms some reasoning benchmarks, BIG-bench sees a huge improvement and general LLM thingys :) submitted by /u/Competitive-Rub-1958 [link] [comments]  ( 4 min )

https://arxiv.org/abs/2111.00600 https://preview.redd.it/l6dni0007jr81.png?width=4888&format=png&auto=webp&s=82c7c9b9433b79c66318090ff85e4535c35ddb18 submitted by /u/inland-1 [link] [comments]  ( 1 min )

CALL FOR PAPERS EvoRL 2022 Evolutionary Reinforcement Learning workshop at GECCO 2022, July 9-13, Boston, USA In recent years reinforcement learning (RL) has received a lot of attention thanks to its performance and ability to address complex tasks. At the same time, multiple recent papers, notably work from OpenAI, have shown that evolution strategies (ES) can be competitive with standard RL algorithms on some problems while being simpler and more scalable. Similar results were obtained by researchers from Uber, this time using a gradient-free genetic algorithm (GA) to train deep neural networks on complex control tasks. Moreover, recent research in the field of evolutionary algorithms (EA) has led to the development of algorithms like Novelty Search and Quality Diversity, capable of…  ( 2 min )

Hey! New Here. I logged back into Reddit after years just to ask this question on this forum. I need to test a model, based loosely on BERT, that classifies a piece of text as having right or left political ideology leaning and whether it promotes any racial or religious stereotypes. For training purpose we used SBIC, IBC, and Stereoset. Though these only contain short sentences which are labeled as belonging to only one of the above categories. Is anyone aware of any other Dataset which can be used for this purpose, which hopefully contains text labeled as promoting or containing a political leaning (left/right, conservative/liberal, neutral) and further either any racial or religious stereotypes? Very thankful in adv submitted by /u/Fee_Imaginary [link] [comments]  ( 1 min )

📑 The Random R-GCN code has just been released! 📝 With just a few lines of code, you can now create embeddings of entities in a Knowledge Graph. ​ Minimal example on how to create embeddings with RR-GCN ​ 💡 RR-GCN does not require training and is competitive to fully trained R-GCNs. 👉 https://github.com/predict-idlab/RR-GCN submitted by /u/givdwiel [link] [comments]

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Logging is a way to store information about your script and track events that occur. When writing any complex script […] The post Logging in Python appeared first on Machine Learning Mastery.  ( 22 min )

As more organizations move to machine learning (ML) to drive deeper insights, two key stumbling blocks they run into are labeling and lifecycle management. Labeling is the identification of data and adding labels to provide context so an ML model can learn from it. Labels might indicate a phrase in an audio file, a car […]  ( 7 min )

Amazon Kendra is an intelligent search service powered by machine learning (ML). Amazon Kendra reimagines search for your websites and applications so your employees and customers can easily find the content they’re looking for, even when it’s scattered across multiple locations and content repositories within your organization. Amazon Kendra supports a variety of document formats, […]  ( 5 min )

Customer service calls require customer agents to have the customer’s account information to process the caller’s request. For example, to provide a status on an insurance claim, the support agent needs policy holder information such as the policy ID and claim number. Such information is often collected in the interactive voice response (IVR) flow at […]  ( 6 min )

Primary focus will be to advance and promote technology, innovation, and entrepreneurship across the school.  ( 4 min )

GPT-3, Codex, and other sizable pre-trained language models can be adjusted to create code from natural language descriptions of programmer intent. Every developer in the world might benefit from these automated models, which have the potential to increase productivity. However, because the models may fail to understand program semantics, the quality of the generated code cannot be guaranteed. Microsoft researchers introduce Jigsaw, a new tool that can help these big language models perform better. Jigsaw is a Python Pandas API code generator that accepts multi-modal inputs. Jigsaw uses post-processing techniques to decipher the syntax and semantics of programs and then uses user feedback to improve future performance. Continue Reading Paper: https://arxiv.org/pdf/2112.02969.pdf Dataset: https://github.com/microsoft/JigsawDataset ​ https://i.redd.it/x223r5qu0kr81.gif submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

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Hi all, Don't know if this is the right subreddit, but here goes.... I'm looking to voice clone my father. He has passed recently, and despite being difficult for all, it's been especially hard for my mother, married early to him and together for 50 years. Her birthday is coming up, I'd love to be able to create a 5-10 second sound byte of him for her. Fortunately, there's likely to be lots of his voice recording around, part of his job was speaking and instructing. So, is there any way this is possible, to be done without great difficulty, and produce an accurate result? I am understanding the moralities of crafting something with his deceased voice. I thought about it quite a bit. However, I feel that it's for his soulmate who's struggling, who he had no qualms spending his life with and travelling abroad with, spent his last days with. I'm certain he would want to help. submitted by /u/mininggotboring [link] [comments]  ( 1 min )

In the previous part, we discussed the current state of data imaging tools in healthcare and the future applications of these technologies. While increased access to information is invaluable to physicians, they can still be limited by their own ability to interpret, or the physical limitations of their surgical ability. In addition to augmenting the… Read More »Data Augmented Healthcare Part 2 The post Data Augmented Healthcare Part 2 appeared first on Data Science Central.  ( 3 min )

Posted by Sharan Narang and Aakanksha Chowdhery, Software Engineers, Google Research In recent years, large neural networks trained for language understanding and generation have achieved impressive results across a wide range of tasks. GPT-3 first showed that large language models (LLMs) can be used for few-shot learning and can achieve impressive results without large-scale task-specific data collection or model parameter updating. More recent LLMs, such as GLaM, LaMDA, Gopher, and Megatron-Turing NLG, achieved state-of-the-art few-shot results on many tasks by scaling model size, using sparsely activated modules, and training on larger datasets from more diverse sources. Yet much work remains in understanding the capabilities that emerge with few-shot learning as we push the limits of …  ( 9 min )

Pekka Varis’s artistry has come a long way from his early days as a self-styled “punk activist” who spray painted during the “old school days of hip hop in Finland.” The post Meet the Omnivore: Videographer Makes Digital Walls, Virtual Homes Pop With NVIDIA Omniverse appeared first on NVIDIA Blog.  ( 3 min )

Hey guys, ​ I really love creating music algorithmically, which is why I have dedicated my master’s thesis to the generation of music patterns by the use of artificial intelligence. In the course of the past 12 months, I have programmed a deep recurrent neural network in Python, which I have trained on 200 self-made music patterns in order to generate somehow novel motifs. ​ In order to evaluate my model, I have set up a short online listening experiment. I’m looking for test subjects right now, so if you are interested in participating, I would really appreciate it. The listening experiment will take you just about 5 to 8 minutes to complete and the only thing you need is a pair of headphones. You can partake on your computer as well as on your smartphone or tablet. ​ Here is the link which gets you to the listening experiment: https://forms.gle/rx1FUQ7RgpjMu1xx9 ​ Thank you very much for taking the time to help me reach my goal. Really appreciate it. submitted by /u/JosephdeLaquinta [link] [comments]  ( 1 min )

Given that most benchmarks for image classification are based on regular, everyday world objects RGB images (or grayscale), what are some unconventional science cases where 2D inputs are substantially different from what we are used to perceive by eye? For example, I'm interested in cases where spatial information can't be constrained to narrow pixel value ranges, such as exponential signals. Or that any standard normalisation (say min-max, zscore) and normalisation layers are not applicable and could lead to the loss of information. One of these cases is Astronomy. However, most practitioners try to to adapt the problem to established standards (say fake RGB images, log scaling flux images, etc). What are other cases out there where the nature of the 2D inputs are very distinct to what we are used to parse through our eyes and what deep nets are benchmarked on? I'm curious about tailored solutions that would intrinsically change the way the deep nets are constructed to solve the research question. submitted by /u/astroferreira [link] [comments]  ( 1 min )

Hi everyone! I'm Atindriyo Sanyal, one of the founders of the ML company Galileo (https://rungalileo.io/). We're building a cool new tool/framework for ML practitioners that helps shine a light on the data you are training your models with. I'd love to get some feedback on the product, and since we're still in private beta, I'm looking for folks to try out the product on their datasets and models. It's easy to use and hooks into popular frameworks such as pyTorch, Tensorflow, Keras, SpaCy etc. Caveat: Currently the tool only works for NLP use cases (think text classification, NER etc). I'll be giving $100 to folks who are willing to give some time to this and provide feedback on the usability of the product. If you're interested, here's a really tiny form (should take <1 minute to fill) for you to fill out. I'll review the applications and send you an email for a follow up Zoom chat where I'll share the software artifacts with you! https://docs.google.com/forms/d/11V20C_J_SyNaX7QL6DasnTe7f0UiueUyaKdmt3xL1oI/edit Look forward and happy (machine) learning! - Atindriyo P.S. If you have any questions or want to chat personally, send me an email at [atin@rungalileo.io](mailto:atin@rungalileo.io). submitted by /u/atindriyo_galileo [link] [comments]  ( 1 min )

Hi everyone, I just launched a GPU compute instance platform (think lambdalabs, fluidstack, aws EC2, vast), and I was wondering what pain points everyone has with existing solutions. I'm not trying to sell anyone anything, but I want to look for feedback that will help me to build a better product. My current thoughts are Ease of getting data into the platform Ease of getting data off of the platform Automation for spinning up and down instances Availability of the type of instance you want Price too high Not enough/too many abstractions TIA and I look forward to some good discussions! submitted by /u/runpod-io [link] [comments]  ( 1 min )

Hello everyone :) We have released last week our paper "Efficient-VDVAE: Less is more" with code! We present simple modifications to the Very Deep VAE to make it converge up to 2.6x times faster and save up to 20x times memory load. We also introduce a gradient smoothing technique to improve stability during training. Our model achieves comparable or better negative log-likelihood (NLL) on 7 commonly used datasets. Additionally, we make an argument against existing 5-bit benchmarks. We empirically show as well that 3% of the latent space is enough to encode the data information without any performance loss. Thus, indicating the potential to efficiently leverage the Hierarchical VAE's latent space in downstream tasks. Paper: https://arxiv.org/abs/2203.13751 Code: https://github.com/Rayhane-mamah/Efficient-VDVAE Paperswithcode: https://paperswithcode.com/paper/efficient-vdvae-less-is-more Feedback is very much appreciated! https://preview.redd.it/tjua1xpq3cr81.png?width=878&format=png&auto=webp&s=718bd91fd648acd673ddab1ad5342207e8be09e7 submitted by /u/Louay-AI [link] [comments]  ( 1 min )

Hey everyone :) We've just released the code for our paper (accepted to CVPR2022) DeepDPM is a nonparametric deep-clustering method which unlike most deep clustering methods, does not require knowing the number of clusters, K; rather, it infers it as a part of the overall learning. Using a split/merge framework to change the clusters number adaptively and a novel loss, our proposed method outperforms existing (both classical and deep) nonparametric methods. While the few existing deep nonparametric methods lack scalability, we show ours by being the first such method that reports its performance on ImageNet. ​ Paper: https://arxiv.org/abs/2203.14309 Code: https://github.com/BGU-CS-VIL/DeepDPM/ Below are some examples of clusters our method found in ImageNet. https://preview.redd.it/jw5kvcuzfbr81.jpg?width=737&format=pjpg&auto=webp&s=5b61cdd0efdea7c92aba611171e5dc7f4276c892 submitted by /u/shahaff32 [link] [comments]  ( 4 min )

Confidence regions are the 2D version of a confidence interval. Almost everywhere in the literature, the shape is elliptic, but no justification is provided. You would think that a confidence region of level γ is defined as the domain of minimum area, covering a mass γ of the underlying probability distribution. That sounds perfectly logical, but it is mentioned nowhere. Based on this definition, the boundary of a confidence region is obtained by solving an optimization problem: it is a problem in calculus of variations -- finding a boundary curve encompassing a domain of minimum area. These problems are usually hard to solve, but in this case, the solution seems trivial: it must be a contour line. And if the underlying distribution is Gaussian, contour lines are obviously ellipses. This would be a solid justification as to why ellipses are so widespread. https://preview.redd.it/42mr1t1je8r81.png?width=1072&format=png&auto=webp&s=2fb9cedbbb15895827ed00edc4912ac39fad0b71 My question here is whether or not my argumentation makes sense, or if there is something faulty in my math. I discuss it in more details in one of my articles, here. If you need clarifications, please reply on Reddit, I will do my best to explain. submitted by /u/MLRecipes [link] [comments]  ( 2 min )

https://www.lesswrong.com/posts/midXmMb2Xg37F2Kgn/new-scaling-laws-for-large-language-models submitted by /u/Singularian2501 [link] [comments]  ( 1 min )

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Artificial intelligence has been long making waves globally, empowering companies from across the broad spectrum of industries to take their businesses to the next level. So it is no surprise that this technology is making inroads in the grocery retail space, helping grocers deliver personalized and irreproachable experiences across different channels, establishing improved customer loyalty,… Read More »Top Ways in Which AI Impacts Grocery Retail The post Top Ways in Which AI Impacts Grocery Retail appeared first on Data Science Central.  ( 3 min )

Data is useless if it doesn’t shed light. The more light it sheds on the most acute problems businesses face, the better. Within this context, data synergy–data from multiple sources and disciplines that is more valuable than the sum of its parts–is often underappreciated. With data synergy, the light can be in many more places,… Read More »A different take on business intelligence The post A different take on business intelligence appeared first on Data Science Central.  ( 4 min )

Blockchain is widely touted as a mechanism for securing digital property. Multiple problems exist for driving metaverse transactions. A new review highlights the challenges, some of which may be insurmountable. Blockchain has been touted as a potential solution to securing users’ digital content and data due to its decentralization, immutability, and transparency. However, there are… Read More »Blockchain Won’t Save The Metaverse The post Blockchain Won’t Save The Metaverse appeared first on Data Science Central.  ( 4 min )

I am currently writing a report on reinforcement learning, where I am trying to describe how the ACER algorithm works. I have read the arxiv paper on the sample actor-critic with experienced replay, but I don't understand where the experience replay comes in. Is this part of the policy gradient? where the policy is updated every episode it's trained on from the previous knowledge it gathers in previous episodes. https://arxiv.org/pdf/1611.01224.pdf ​ submitted by /u/beepingwater_neko [link] [comments]  ( 1 min )

Sorry if this post is not appropriate here, but I have been wondering how can I implement and learn a decision tree or any other non differentiable function approximators for the Value Function. It’s relatively easy to formulate and use DQN type algorithms by using neural network and say pytorch + stochastic optimization but I want to try out some tree based methods. (at least to reproduce papers which claim to use them) But I don’t know 1) If we have to design the structures and learning algorithms by hand or is there any package I can use? 2) How should the learning be done? We obviously can’t go regression type learning because of the bootstrapping nature of the Bellman equation? Thanks submitted by /u/Htaseht [link] [comments]  ( 1 min )

The episode step count is the same for training and testing. submitted by /u/Gleann_na_nGealt [link] [comments]  ( 1 min )

I tried using RL for some work at my university, it did not really work all that well. I'm wondering if there are some real life scenarios that I could use to create my own personal projects. Otherwise, I'd be fine with games. I want to try all of it out from Dynamic Programming to crazy ass stuff like A3C, PPO and so on. I like RL, more so than any other form of ML, and I want to play around with it as a hobby. This is really the area of ML for me. For starters, there are fundamentals behind it, so you can mostly explain why agents do one thing or another. Additionally, there isn't a need to have massive amounts of data. It's also the only type of ML that I've been able to successfully use with software engineering. Designing the agent and the API it uses to take actions in an environment is as much a software engineering project as is creating a REST API. I feel there is a lot of potential for me to go crazy with this, and I was wondering if people have any cool suggestions. Anything that is real time is anything that I want to do. Real time systems and RL are exactly the sort of thing I love to do. submitted by /u/HesperusIII [link] [comments]  ( 2 min )

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Im a 19yo guy from Argentina that studies system ingeneer, I like my career, beeing an ingeneer is great, but coding and AI is greater, Im tired of courses like Free code academy, or basics things, im looking for a more professional, useful and deeper courses, that will really teach me, im currently with python(pandas,numpy,matplotlib,tensorflow) basics, and wanna to be better in that field that i love❤ submitted by /u/Sasulanda [link] [comments]  ( 1 min )

What are the most active non-ML/statistical research areas in AI? Are there any recent books published that give an overview of such areas? Seems like AI is now either ML or people saying that ML won’t work, but vague on alternatives. submitted by /u/spookyplatypus [link] [comments]  ( 1 min )

Imagine being able to tell a machine to write an app simply by telling it what the app does. As far-fetched as it may appear, this scenario is already a reality. According to Salesforce AI Research, conversational AI programming is a new paradigm that brings this vision to life, thanks to an AI system that builds software. Introducing CodeGen: Creating Programs from Prompts The large-scale language model, CodeGen, which converts simple English prompts into executable code, is the first step toward this objective. The person doesn’t write any code; instead, (s)he describes what (s)he wants the code to perform in normal language, and the computer does the rest. Conversational AI refers to technologies that allow a human and a computer to engage naturally through a conversation. Chatbots, voice assistants, and virtual agents are examples of conversational AI. Continue Reading Paper: https://arxiv.org/pdf/2203.13474.pdf Github: https://github.com/salesforce/CodeGen https://i.redd.it/dbyba3dct8r81.gif submitted by /u/No_Coffee_4638 [link] [comments]  ( 2 min )

For the MIT Schwarzman College of Computing dean, bringing disciplines together is the best way to address challenges and opportunities posed by rapid advancements in computing.  ( 8 min )

The sensor to shooter timeline is affected by two main variables: satellite positioning and asset positioning. Speeding up satellite positioning by adding more sensors or by decreasing processing time is important only if there is a prepared shooter, otherwise the main source of time is getting the shooter into position. However, the intelligence community should work towards the exploitation of sensors to the highest speed and effectiveness possible. Achieving a high effectiveness while keeping speed high is a tradeoff that must be considered in the sensor to shooter timeline. In this paper we investigate two main ideas, increasing the effectiveness of satellite imagery through image manipulation and how on-board image manipulation would affect the sensor to shooter timeline. We cover these ideas in four scenarios: Discrete Event Simulation of onboard processing versus ground station processing, quality of information with cloud cover removal, information improvement with super resolution, and data reduction with image to caption. This paper will show how image manipulation techniques such as Super Resolution, Cloud Removal, and Image to Caption will improve the quality of delivered information in addition to showing how those processes effect the sensor to shooter timeline.  ( 2 min )

AgentZero https://github.com/zhoubin-me/agent0 This is my personal project developed two years ago. It covers major DRL algorithms like: - [DQN](https://arxiv.org/abs/1312.5602)- [Double DQN](https://arxiv.org/abs/1509.06461)- [Dueling DQN](https://arxiv.org/abs/1511.06581)- [Prioritized Experience Replay](https://arxiv.org/abs/1511.05952)- [Noisy Network](https://arxiv.org/abs/1706.10295)- [C51](https://arxiv.org/abs/1707.06887)- [Rainbow](https://arxiv.org/abs/1710.02298)- [QR-DQN](https://arxiv.org/abs/1710.10044)- [IQR](https://arxiv.org/abs/1806.06923)- [FQF](https://arxiv.org/abs/1911.02140)- [DDPG](https://arxiv.org/abs/1509.02971)- [SAC](https://arxiv.org/abs/1801.01290)- [TD3](https://arxiv.org/abs/1802.09477)- [MDQN](https://arxiv.org/abs/2007.14430) What is amazing is its speed and memory efficiency after some optimization: With a single 2080Ti GPU and a 8 core AMD CPU, the training speed of rainbow for Atari could achieve 3000FPS, which means it can finish training of 10M frames within 1 hour. With compression of image frames, replay memory's RAM usage is down by 20%. I have tested several algorithms and games on Atari and get some initial result. Welcome to use and contribute! submitted by /u/zhoubin-me [link] [comments]  ( 1 min )

I am searching for regularization methods applied to DRL algorithms (either value or policy-based) to understand what has been done so far in the field. I cannot find any valid reference that studies the effect of applying a soft constraint to the reward function instead of to the policy objective. This may seem useless for some applications, but it is highly relevant for finance, which is my domain of application, The idea I have so far is that if you constrain the reward, it is like you are imposing limits on the agent's behavior. On the other hand, if you constrain the objective, you are not limiting the behavior, but you are correcting the ex-post the undesired behavior. The latter way does not allow the agent to learn not to behave in a certain way. ​ Did anyone ever think about it? Are they good references that analyze the different effects of a constraint to whatever DRL algorithm? submitted by /u/alebrini [link] [comments]  ( 1 min )

I am looking at this code from Google (https://github.com/google-research/google-research/blob/master/social_rl/multiagent_tfagents/joint_attention/attention_networks.py). At line 639, the LSTM is called. The first two inputs are the state and the network state, but I don't understand what the latter is. submitted by /u/No_Possibility_7588 [link] [comments]  ( 1 min )

I'm part of a group of researchers from top ML institutions and industry, our goal is to figure out how improve efficiency in DeepRL. We are looking at Rubik’s Cube as target problem, and kicking off a project which will start from https://github.com/forestagostinelli/DeepCubeA and go from there. Prior works require hand crafted curriculum and billion of interactions to solve a cube, we believe that order of magnitude more compute that it should take. Is anyone interested to collaborate? I'm happy to dedicate a few hours a week to help a newcomer like I was a few years ago with the RL stuff given some basics of machine learning and programming skills, and this could be the golden opportunity for someone to see RL at scale. submitted by /u/mind_library [link] [comments]  ( 2 min )

I'm absolutely new to machine learning, let alone reinforcement learning. I've been tasked to replicate and if possible improve upon the paper linked in the post. I don't know what platform to use and how to create the custom environment. if anybody could share any resources it would be tremendously helpful. https://drive.google.com/file/d/1fIT43hKi61WUIvoTh2a3AWlRsphi-L98/view?usp=sharing submitted by /u/Lazarus_07 [link] [comments]  ( 1 min )

Trying to wrap my head around the RL essentials. Would it be correct to say that Q-learning attempts to select the best available policy by optimizing the Q-function, while policy gradient methods work directly to optimize a pre-determined policy's parameters? submitted by /u/JimBeanery [link] [comments]  ( 1 min )

I noticed most DRL papers use very shallow models like three or four layers. However, when I try to do DRL tasks that have relatively complicated scenes (for example, some modern video game), shallow models become way too weak. Are there papers, blogs, articles etc. that use more complex/deep models? Or maybe some methods that can deal with complicated scenes without deep models? Thanks submitted by /u/seermer [link] [comments]  ( 1 min )

Hi all, I have an thought on A universal parameter optimizer I wanted to share with you and to see if you know some related work. Assume you have a simulation or access to an environment. There are certain parameters you can set to control the performance of a system which lives in this simulation/environment. Naturally, one wants to find the optimal parameters or optimal policy to set the parameter that can result the most reward, however that is defined. For example, in the stock market, I may want to find the optimal market price to buy and sell, or the optimal policy. In a car driving game, I may want to determine the optimal policy to set speed and direction. Do we know if there is formal way to study this type of problem? Thank you! submitted by /u/DB8868 [link] [comments]  ( 1 min )

Artificial Intelligence is being used in almost every aspect of life. AI symbolizes growth and productivity in the minds of some, but it is raising questions as well on the fairness, privacy, and security of these systems. Many legitimate issues exist, including biased choices, labor replacement, and a lack of security. When it comes to robots, this is very frightening. Self-driving automobiles, for example, can cause injury or death if they make mistakes. Responsible AI addresses these difficulties and makes AI systems more accountable. Responsible AI should fulfill the following aims: Interpretability: We obtain an explanation for how a model makes predictions when we interpret it. An AI system makes predictions for a user. Even if these selections are correct, a user is likely to seek an explanation. Responsible AI can describe how we create interpretable models. Fairness: AI systems have the potential to make judgments that are biased towards particular groups of people. Bias in the training data is the source of this bias. The easier it is to assure fairness and rectify any bias in a model, the more interpretable it is. As a result, we need a Responsible AI framework to explain how we evaluate fairness and what to do if a model makes unjust predictions. Safety and Security: AI systems aren’t deterministic. When confronted with new situations, they are prone to making poor choices. The systems can even be tampered with to make unwise decisions. Therefore, we need to ensure safety and security in these systems. Data Governance: The data used must be of high quality. If the data used by AI has errors, the system may make wrong decisions. Continue Reading The Article Here ​ https://preview.redd.it/9iivp31ir6r81.png?width=1024&format=png&auto=webp&s=207409694b68a1e985ad1dfcf3b466ac25916da2 submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

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Artificial Intelligence and deep learning in video analytics are gaining popularity. It has enabled a wide range of industrial applications, including surveillance and public safety, robotics perception, medical intervention, and facial recognition. According to Markets & Markets, the global market for video analytics was valued at USD 5.9 billion in 2021 and is predicted to reach USD 14.9 billion by 2026. Unmanned aerial vehicles (UAVs) have also enabled a wide range of video analytics applications (e.g., aerial surveys) since they provide aerial views of the environment, allowing for collecting aerial photos and processing with deep learning algorithms. Parking analytics is one of these critical smart city applications that uses deep learning and UAVs to collect real-time data and analyze it in order to maximize parking revenue, enhance parking resource allocations, and better manage public space. Continue Reading Paper: https://arxiv.org/pdf/2203.07792.pdf ​ https://i.redd.it/u5th7z0ja5r81.gif submitted by /u/No_Coffee_4638 [link] [comments]  ( 1 min )

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Electrical and mechanical oscillations satisfy analogous equations. This is the basis of using the word “analog” in electronics. You could study a mechanical system by building an analogous circuit and measuring that circuit in a lab. Mass, dashpot, spring Years ago I wrote a series of four posts about mechanical vibrations: Free, undamped vibrations Free, […] Oscillations in RLC circuits first appeared on John D. Cook.  ( 2 min )

Well I' ve been reading some scientific works and I don't understand how nomograms are constructed from logistic regression models. In this example I have: https://ieeexplore-1ieee-1org-1000007l206e9.han.bg.pg.edu.pl/document/9514609 And they train LR model on Covid19 dataset [death/ didn't die] so it's binary classification problem However later on, they construct nomogram, which determines whether there is low/moderate/high risk of covid19 mortality. What I don't undestand is how they calculate the score the establish chances of death. E.G. If the score is <0.05 there is low possibility that patient will die. My general question is, how they constructed this nomogram from the binary classifier they had? submitted by /u/s168501 [link] [comments]  ( 1 min )

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Hello everyone, there is a lot of work in the field of geometric deep learning for combinatorial optimization that yields good approximation algorithms for "hard" problems on graphs (see here), with the most prominent example being the TSP problem. However, as far as I can see, all these considered problems share the fact that the computed solution is a subset of the vertices/edges of the original graph. In my field (graph drawing), one of the most important considered properties is the crossing number). Hence, the solution would not consists of a labeling of the edges/vertices, but is rather a regression task on the whole graph. I have a dataset that consists of roughly 10000 graphs together with their crossing number. Treating the above problem as a supervised regression task and simply inserting the graph into a GNN does not work for me at all - is this a problem of my choice of architecture or is this sort of "function" that maps a graph to its crossing number something we can expect no current architecture to find. I appreciate any comment, even if it is just your intuition on the problem. Best regards, MrLemming submitted by /u/MrLemming2 [link] [comments]  ( 1 min )

Dear community, I hope I do not violate rules by advertizing our Call for Papers here. In a nutshell, submissions can be robustness or OOD datasets and new metrics which we will consolidate in one benchmark. More infos on our website. I am happy to answer any questions in regards to the call. submitted by /u/helavisa4 [link] [comments]  ( 1 min )

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I was looking at this paper (https://arxiv.org/pdf/2005.14165.pdf) and came across this graph: ​ https://preview.redd.it/49ydy9bo61r81.png?width=665&format=png&auto=webp&s=729743449d6a99d2b84b81610e7e32d87ea4dfeb I am trying to understand the following two things about this graph: ​ What is PetaFLOP/s-days? I read that a PetaFLOP is 1,000,000,000,000,000 calculations (e.g. addition, subtraction). I am guessing that 10^2 would imply 100 * 1,000,000,000,000,000 calculations per day - is this correct? Is there any difference between PetaFLOP/days and PetaFLOP/s-days? (I also find it interesting they are probably referring to "computer resources" as simply "compute") What does "C" stand for in L = 2.57 * C^-0.048? I am guessing that the "dotted line" probably refers to the "average loss" for different Neural Networks with differing amounts of Parameters - but what exactly does "C" stand for? Finally, is there a reason that "Validation Loss" is not expressed as a percentage? For instance, what is a Validation Loss of 3? Is a Validation Loss of 3 the same as a Loss of 30%? Or does Validation Loss simply refer to the value of the Loss Function obtained during the Validation stage of Cross Validation? Thank you! submitted by /u/blueest [link] [comments]  ( 2 min )

In my previous post, I briefly described how leading companies use experimentation to optimize their products and services and evolve them to the point of feeling elegant, efficient, and magical. These companies have developed mature experimentation programs (ExPrs), including the… Read More The post What is an Experimentation program and Who is Involved? (Experimentation Program Series: Guide 02) appeared first on ML in Production.  ( 6 min )

In my previous post, I briefly described how leading companies use experimentation to optimize their products and services and evolve them to the point of feeling elegant, efficient, and magical. These companies have developed mature experimentation programs (ExPrs), including the… Read More The post What is an Experimentation program and Who is Involved? (Experimentation Program Series: Guide 02) appeared first on ML in Production.  ( 6 min )

submitted by /u/cjmodi306 [link] [comments]

Hey, guys. Just want to ask if anybody's interested with a C++ machine learning book, "Hands-on Machine Learning with C++" by Kirill Kolodiazhnyi. If you are, send me a DM. submitted by /u/edmondgrasa [link] [comments]

As the title says, I would like to know/get some direction to the question when in general does and effnet is preferred to a resnet? I understand that the paper compares performances and it shows a higher performance wrt every network. So my question would be is that always the case or is there a specific situation where it would be better? Sorry for the typos (on my mobile) submitted by /u/johnyj01 [link] [comments]  ( 1 min )

We introduce MyStyle, a personalized deep generative prior trained with a few shots of an individual. MyStyle allows to reconstruct, enhance and edit images of a specific person, such that the output is faithful to the person's key facial characteristics. Given a small reference set of portrait images of a person (~100), we tune the weights of a pretrained StyleGAN face generator to form a local, low-dimensional, personalized manifold in the latent space. We show that this manifold constitutes a personalized region that spans latent codes associated with diverse portrait images of the individual. Moreover, we demonstrate that we obtain a personalized generative prior, and propose a unified approach to apply it to various ill-posed image enhancement problems, such as inpainting and super-resolution, as well as semantic editing. Using the personalized generative prior we obtain outputs that exhibit high-fidelity to the input images and are also faithful to the key facial characteristics of the individual in the reference set. We demonstrate our method with fair-use images of numerous widely recognizable individuals for whom we have the prior knowledge for a qualitative evaluation of the expected outcome. We evaluate our approach against few-shots baselines and show that our personalized prior, quantitatively and qualitatively, outperforms state-of-the-art alternatives.  ( 2 min )

We propose a novel policy update that combines regularized policy optimization with model learning as an auxiliary loss. The update (henceforth Muesli) matches MuZero's state-of-the-art performance on Atari. Notably, Muesli does so without using deep search: it acts directly with a policy network and has computation speed comparable to model-free baselines. The Atari results are complemented by extensive ablations, and by additional results on continuous control and 9x9 Go.  ( 2 min )

Speaker identification systems in a real-world scenario are tasked to identify a speaker amongst a set of enrolled speakers given just a few samples for each enrolled speaker. This paper demonstrates the effectiveness of meta-learning and relation networks for this use case. We propose improved relation networks for speaker verification and few-shot (unseen) speaker identification. The use of relation networks facilitates joint training of the frontend speaker encoder and the backend model. Inspired by the use of prototypical networks in speaker verification and to increase the discriminability of the speaker embeddings, we train the model to classify samples in the current episode amongst all speakers present in the training set. Furthermore, we propose a new training regime for faster model convergence by extracting more information from a given meta-learning episode with negligible extra computation. We evaluate the proposed techniques on VoxCeleb, SITW and VCTK datasets on the tasks of speaker verification and unseen speaker identification. The proposed approach outperforms the existing approaches consistently on both tasks.  ( 2 min )

Convolutional Neural Networks (CNNs) models are one of the most frequently used deep learning networks, and extensively used in both academia and industry. Recent studies demonstrated that adversarial attacks against such models can maintain their effectiveness even when used on models other than the one targeted by the attacker. This major property is known as transferability, and makes CNNs ill-suited for security applications. In this paper, we provide the first comprehensive study which assesses the robustness of CNN-based models for computer networks against adversarial transferability. Furthermore, we investigate whether the transferability property issue holds in computer networks applications. In our experiments, we first consider five different attacks: the Iterative Fast Gradient Method (I-FGSM), the Jacobian-based Saliency Map (JSMA), the Limited-memory Broyden Fletcher Goldfarb Shanno BFGS (L- BFGS), the Projected Gradient Descent (PGD), and the DeepFool attack. Then, we perform these attacks against three well- known datasets: the Network-based Detection of IoT (N-BaIoT) dataset, the Domain Generating Algorithms (DGA) dataset, and the RIPE Atlas dataset. Our experimental results show clearly that the transferability happens in specific use cases for the I- FGSM, the JSMA, and the LBFGS attack. In such scenarios, the attack success rate on the target network range from 63.00% to 100%. Finally, we suggest two shielding strategies to hinder the attack transferability, by considering the Most Powerful Attacks (MPAs), and the mismatch LSTM architecture.  ( 2 min )

The improvements in the edge computing technology pave the road for diversified applications that demand real-time interaction. However, due to the mobility of the end-users and the dynamic edge environment, it becomes challenging to handle the task offloading with high performance. Moreover, since each application in mobile devices has different characteristics, a task orchestrator must be adaptive and have the ability to learn the dynamics of the environment. For this purpose, we develop a deep reinforcement learning based task orchestrator, DeepEdge, which learns to meet different task requirements without needing human interaction even under the heavily-loaded stochastic network conditions in terms of mobile users and applications. Given the dynamic offloading requests and time-varying communication conditions, we successfully model the problem as a Markov process and then apply the Double Deep Q-Network (DDQN) algorithm to implement DeepEdge. To evaluate the robustness of DeepEdge, we experiment with four different applications including image rendering, infotainment, pervasive health, and augmented reality in the network under various loads. Furthermore, we compare the performance of our agent with the four different task offloading approaches in the literature. Our results show that DeepEdge outperforms its competitors in terms of the percentage of satisfactorily completed tasks.  ( 2 min )

Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is a important challenge in artificial intelligence. Two key approaches to this problem are reinforcement learning (RL) and planning. This paper presents a survey of the integration of both fields, better known as model-based reinforcement learning. Model-based RL has two main steps. First, we systematically cover approaches to dynamics model learning, including challenges like dealing with stochasticity, uncertainty, partial observability, and temporal abstraction. Second, we present a systematic categorization of planning-learning integration, including aspects like: where to start planning, what budgets to allocate to planning and real data collection, how to plan, and how to integrate planning in the learning and acting loop. After these two sections, we also discuss implicit model-based RL as an end-to-end alternative for model learning and planning, and we cover the potential benefits of model-based RL. Along the way, the survey also draws connections to several related RL fields, like hierarchical RL and transfer learning. Altogether, the survey presents a broad conceptual overview of the combination of planning and learning for MDP optimization.  ( 2 min )

Recent advances like StyleGAN have promoted the growth of controllable facial editing. To address its core challenge of attribute decoupling in a single latent space, attempts have been made to adopt dual-space GAN for better disentanglement of style and content representations. Nonetheless, these methods are still incompetent to obtain plausible editing results with high controllability, especially for complicated attributes. In this study, we highlight the importance of interaction in a dual-space GAN for more controllable editing. We propose TransEditor, a novel Transformer-based framework to enhance such interaction. Besides, we develop a new dual-space editing and inversion strategy to provide additional editing flexibility. Extensive experiments demonstrate the superiority of the proposed framework in image quality and editing capability, suggesting the effectiveness of TransEditor for highly controllable facial editing.  ( 2 min )

Sequential decision making, commonly formalized as optimization of a Markov Decision Process, is a key challenge in artificial intelligence. Two successful approaches to MDP optimization are reinforcement learning and planning, which both largely have their own research communities. However, if both research fields solve the same problem, then we might be able to disentangle the common factors in their solution approaches. Therefore, this paper presents a unifying algorithmic framework for reinforcement learning and planning (FRAP), which identifies underlying dimensions on which MDP planning and learning algorithms have to decide. At the end of the paper, we compare a variety of well-known planning, model-free and model-based RL algorithms along these dimensions. Altogether, the framework may help provide deeper insight in the algorithmic design space of planning and reinforcement learning.  ( 2 min )

In this paper we study data augmentation for opcode sequence based Android malware detection. Data augmentation has been successfully used in many areas of deep-learning to significantly improve model performance. Typically, data augmentation simulates realistic variations in data to increase the apparent diversity of the training-set. However, for opcode-based malware analysis it is not immediately clear how to apply data augmentation. Hence we first study the use of fixed transformations, then progress to adaptive methods. We propose a novel data augmentation method -- Self-Embedding Language Model Augmentation -- that uses a malware detection network's own opcode embedding layer to measure opcode similarity for adaptive augmentation. To the best of our knowledge this is the first paper to carry out a systematic study of different augmentation methods for opcode sequence based Android malware classification.  ( 2 min )

Machine learning has developed a variety of tools for learning and representing high-dimensional distributions with structure. Recent years have also seen big advances in designing multi-item mechanisms. Akin to overfitting, however, these mechanisms can be extremely sensitive to the Bayesian prior that they target, which becomes problematic when that prior is only approximately known. At the same time, even if access to the exact Bayesian prior is given, it is known that optimal or even approximately optimal multi-item mechanisms run into sample, computational, representation and communication intractability barriers. We consider a natural class of multi-item mechanism design problems with very large numbers of items, but where the bidders' value distributions can be well-approximated by a topic model akin to those used in recommendation systems with very large numbers of possible recommendations. We propose a mechanism design framework for this setting, building on a recent robustification framework by Brustle et al., which disentangles the statistical challenge of estimating a multi-dimensional prior from the task of designing a good mechanism for it, and robustifies the performance of the latter against the estimation error of the former. We provide an extension of this framework appropriate for our setting, which allows us to exploit the expressive power of topic models to reduce the effective dimensionality of the mechanism design problem and remove the dependence of its computational, communication and representation complexity on the number of items.  ( 2 min )

Automatically mining sentiment tendency contained in natural language is a fundamental research to some artificial intelligent applications, where solutions alternate with challenges. Transfer learning and multi-task learning techniques have been leveraged to mitigate the supervision sparsity and collaborate multiple heterogeneous domains correspondingly. Recent years, the sensitive nature of users' private data raises another challenge for sentiment classification, i.e., data privacy protection. In this paper, we resort to federated learning for multiple domain sentiment classification under the constraint that the corpora must be stored on decentralized devices. In view of the heterogeneous semantics across multiple parties and the peculiarities of word embedding, we pertinently provide corresponding solutions. First, we propose a Knowledge Transfer Enhanced Private-Shared (KTEPS) framework for better model aggregation and personalization in federated sentiment classification. Second, we propose KTEPS$^\star$ with the consideration of the rich semantic and huge embedding size properties of word vectors, utilizing Projection-based Dimension Reduction (PDR) methods for privacy protection and efficient transmission simultaneously. We propose two federated sentiment classification scenes based on public benchmarks, and verify the superiorities of our proposed methods with abundant experimental investigations.  ( 2 min )

The use of machine learning (ML) in high-stakes societal decisions has encouraged the consideration of fairness throughout the ML lifecycle. Although data integration is one of the primary steps to generate high quality training data, most of the fairness literature ignores this stage. In this work, we consider fairness in the integration component of data management, aiming to identify features that improve prediction without adding any bias to the dataset. We work under the causal interventional fairness paradigm. Without requiring the underlying structural causal model a priori, we propose an approach to identify a sub-collection of features that ensure the fairness of the dataset by performing conditional independence tests between different subsets of features. We use group testing to improve the complexity of the approach. We theoretically prove the correctness of the proposed algorithm to identify features that ensure interventional fairness and show that sub-linear conditional independence tests are sufficient to identify these variables. A detailed empirical evaluation is performed on real-world datasets to demonstrate the efficacy and efficiency of our technique.  ( 2 min )

Multi-agent reinforcement learning has made substantial empirical progresses in solving games with a large number of players. However, theoretically, the best known sample complexity for finding a Nash equilibrium in general-sum games scales exponentially in the number of players due to the size of the joint action space, and there is a matching exponential lower bound. This paper investigates what learning goals admit better sample complexities in the setting of $m$-player general-sum Markov games with $H$ steps, $S$ states, and $A_i$ actions per player. First, we design algorithms for learning an $\epsilon$-Coarse Correlated Equilibrium (CCE) in $\widetilde{\mathcal{O}}(H^5S\max_{i\le m} A_i / \epsilon^2)$ episodes, and an $\epsilon$-Correlated Equilibrium (CE) in $\widetilde{\mathcal{O}}(H^6S\max_{i\le m} A_i^2 / \epsilon^2)$ episodes. This is the first line of results for learning CCE and CE with sample complexities polynomial in $\max_{i\le m} A_i$. Our algorithm for learning CE integrates an adversarial bandit subroutine which minimizes a weighted swap regret, along with several novel designs in the outer loop. Second, we consider the important special case of Markov Potential Games, and design an algorithm that learns an $\epsilon$-approximate Nash equilibrium within $\widetilde{\mathcal{O}}(S\sum_{i\le m} A_i / \epsilon^3)$ episodes (when only highlighting the dependence on $S$, $A_i$, and $\epsilon$), which only depends linearly in $\sum_{i\le m} A_i$ and significantly improves over existing efficient algorithm in the $\epsilon$ dependence. Overall, our results shed light on what equilibria or structural assumptions on the game may enable sample-efficient learning with many players.  ( 2 min )

We introduce a block-online variant of the temporal feature-wise linear modulation (TFiLM) model to achieve bandwidth extension. The proposed architecture simplifies the UNet backbone of the TFiLM to reduce inference time and employs an efficient transformer at the bottleneck to alleviate performance degradation. We also utilize self-supervised pretraining and data augmentation to enhance the quality of bandwidth extended signals and reduce the sensitivity with respect to downsampling methods. Experiment results on the VCTK dataset show that the proposed method outperforms several recent baselines in both intrusive and non-intrusive metrics. Pretraining and filter augmentation also help stabilize and enhance the overall performance.  ( 2 min )

Evolution strategies (ES), as a family of black-box optimization algorithms, recently emerge as a scalable alternative to reinforcement learning (RL) approaches such as Q-learning or policy gradient, and are much faster when many central processing units (CPUs) are available due to better parallelization. In this paper, we propose a systematic incremental learning method for ES in dynamic environments. The goal is to adjust previously learned policy to a new one incrementally whenever the environment changes. We incorporate an instance weighting mechanism with ES to facilitate its learning adaptation, while retaining scalability of ES. During parameter updating, higher weights are assigned to instances that contain more new knowledge, thus encouraging the search distribution to move towards new promising areas of parameter space. We propose two easy-to-implement metrics to calculate the weights: instance novelty and instance quality. Instance novelty measures an instance's difference from the previous optimum in the original environment, while instance quality corresponds to how well an instance performs in the new environment. The resulting algorithm, Instance Weighted Incremental Evolution Strategies (IW-IES), is verified to achieve significantly improved performance on challenging RL tasks ranging from robot navigation to locomotion. This paper thus introduces a family of scalable ES algorithms for RL domains that enables rapid learning adaptation to dynamic environments.  ( 2 min )

Iterating with new and improved OCR solutions enforces decision making when it comes to targeting the right candidates for reprocessing. This especially applies when the underlying data collection is of considerable size and rather diverse in terms of fonts, languages, periods of publication and consequently OCR quality. This article captures the efforts of the National Library of Luxembourg to support those targeting decisions. They are crucial in order to guarantee low computational overhead and reduced quality degradation risks, combined with a more quantifiable OCR improvement. In particular, this work explains the methodology of the library with respect to text block level quality assessment. Through extension of this technique, a regression model, that is able to take into account the enhancement potential of a new OCR engine, is also presented. They both mark promising approaches, especially for cultural institutions dealing with historical data of lower quality.  ( 2 min )

Training a multi-speaker Text-to-Speech (TTS) model from scratch is computationally expensive and adding new speakers to the dataset requires the model to be re-trained. The naive solution of sequential fine-tuning of a model for new speakers can lead to poor performance of older speakers. This phenomenon is known as catastrophic forgetting. In this paper, we look at TTS modeling from a continual learning perspective, where the goal is to add new speakers without forgetting previous speakers. Therefore, we first propose an experimental setup and show that serial fine-tuning for new speakers can cause the forgetting of the earlier speakers. Then we exploit two well-known techniques for continual learning, namely experience replay and weight regularization. We reveal how one can mitigate the effect of degradation in speech synthesis diversity in sequential training of new speakers using these methods. Finally, we present a simple extension to experience replay to improve the results in extreme setups where we have access to very small buffers.  ( 2 min )

A substantial body of empirical work documents the lack of robustness in deep learning models to adversarial examples. Recent theoretical work proved that adversarial examples are ubiquitous in two-layers networks with sub-exponential width and ReLU or smooth activations, and multi-layer ReLU networks with sub-exponential width. We present a result of the same type, with no restriction on width and for general locally Lipschitz continuous activations. More precisely, given a neural network $f(\,\cdot\,;{\boldsymbol \theta})$ with random weights ${\boldsymbol \theta}$, and feature vector ${\boldsymbol x}$, we show that an adversarial example ${\boldsymbol x}'$ can be found with high probability along the direction of the gradient $\nabla_{{\boldsymbol x}}f({\boldsymbol x};{\boldsymbol \theta})$. Our proof is based on a Gaussian conditioning technique. Instead of proving that $f$ is approximately linear in a neighborhood of ${\boldsymbol x}$, we characterize the joint distribution of $f({\boldsymbol x};{\boldsymbol \theta})$ and $f({\boldsymbol x}';{\boldsymbol \theta})$ for ${\boldsymbol x}' = {\boldsymbol x}-s({\boldsymbol x})\nabla_{{\boldsymbol x}}f({\boldsymbol x};{\boldsymbol \theta})$.  ( 2 min )

The field of machine learning has achieved striking progress in recent years, witnessing breakthrough results on language modelling, protein folding and nitpickingly fine-grained dog breed classification. Some even succeeded at playing computer games and board games, a feat both of engineering and of setting their employers' expectations. The central contribution of this work is to carefully examine whether this progress, and technology more broadly, can be expected to continue indefinitely. Through a rigorous application of statistical theory and failure to extrapolate beyond the training data, we answer firmly in the negative and provide details: technology will peak at 3:07 am (BST) on 20th July, 2032. We then explore the implications of this finding, discovering that individuals awake at this ungodly hour with access to a sufficiently powerful computer possess an opportunity for myriad forms of long-term linguistic 'lock in'. All we need is a large (>> 1W) data centre to seize this pivotal moment. By setting our analogue alarm clocks, we propose a tractable algorithm to ensure that, for the future of humanity, the British spelling of colour becomes the default spelling across more than 80% of the global word processing software market.  ( 2 min )

We present a novel feasibility study on the automatic recognition of Expressed Emotion (EE), a family environment concept based on caregivers speaking freely about their relative/family member. We describe an automated approach for determining the \textit{degree of warmth}, a key component of EE, from acoustic and text features acquired from a sample of 37 recorded interviews. These recordings, collected over 20 years ago, are derived from a nationally representative birth cohort of 2,232 British twin children and were manually coded for EE. We outline the core steps of extracting usable information from recordings with highly variable audio quality and assess the efficacy of four machine learning approaches trained with different combinations of acoustic and text features. Despite the challenges of working with this legacy data, we demonstrated that the degree of warmth can be predicted with an $F_{1}$-score of \textbf{61.5\%}. In this paper, we summarise our learning and provide recommendations for future work using real-world speech samples.

Breakthroughs in transformer-based models have revolutionized not only the NLP field, but also vision and multimodal systems. However, although visualization and interpretability tools have become available for NLP models, internal mechanisms of vision and multimodal transformers remain largely opaque. With the success of these transformers, it is increasingly critical to understand their inner workings, as unraveling these black-boxes will lead to more capable and trustworthy models. To contribute to this quest, we propose VL-InterpreT, which provides novel interactive visualizations for interpreting the attentions and hidden representations in multimodal transformers. VL-InterpreT is a task agnostic and integrated tool that (1) tracks a variety of statistics in attention heads throughout all layers for both vision and language components, (2) visualizes cross-modal and intra-modal attentions through easily readable heatmaps, and (3) plots the hidden representations of vision and language tokens as they pass through the transformer layers. In this paper, we demonstrate the functionalities of VL-InterpreT through the analysis of KD-VLP, an end-to-end pretraining vision-language multimodal transformer-based model, in the tasks of Visual Commonsense Reasoning (VCR) and WebQA, two visual question answering benchmarks. Furthermore, we also present a few interesting findings about multimodal transformer behaviors that were learned through our tool.

Wind energy is one of the cleanest renewable electricity sources and can help in addressing the challenge of climate change. One of the drawbacks of wind-generated energy is the large space necessary to install a wind farm; this arises from the fact that placing wind turbines in a limited area would hinder their productivity and therefore not be economically convenient. This naturally leads to an optimisation problem, which has three specific challenges: (1) multiple conflicting objectives (2) computationally expensive simulation models and (3) optimisation over design sets instead of design vectors. The first and second challenges can be addressed by using surrogate-assisted e.g.\ Bayesian multi-objective optimisation. However, the traditional Bayesian optimisation cannot be applied as the optimisation function in the problem relies on design sets instead of design vectors. This paper extends the applicability of Bayesian multi-objective optimisation to set based optimisation for solving the wind farm layout problem. We use a set-based kernel in Gaussian process to quantify the correlation between wind farms (with a different number of turbines). The results on the given data set of wind energy and direction clearly show the potential of using set-based Bayesian multi-objective optimisation.

Deep learning has achieved tremendous success with independent and identically distributed (i.i.d.) data. However, the performance of neural networks often degenerates drastically when encountering out-of-distribution (OoD) data, i.e., when training and test data are sampled from different distributions. While a plethora of algorithms have been proposed for OoD generalization, our understanding of the data used to train and evaluate these algorithms remains stagnant. In this work, we first identify and measure two distinct kinds of distribution shifts that are ubiquitous in various datasets. Next, through extensive experiments, we compare OoD generalization algorithms across two groups of benchmarks, each dominated by one of the distribution shifts, revealing their strengths on one shift as well as limitations on the other shift. Overall, we position existing datasets and algorithms from different research areas seemingly unconnected into the same coherent picture. It may serve as a foothold that can be resorted to by future OoD generalization research. Our code is available at https://github.com/ynysjtu/ood_bench.

Current text-to-speech algorithms produce realistic fakes of human voices, making deepfake detection a much-needed area of research. While researchers have presented various techniques for detecting audio spoofs, it is often unclear exactly why these architectures are successful: Preprocessing steps, hyperparameter settings, and the degree of fine-tuning are not consistent across related work. Which factors contribute to success, and which are accidental? In this work, we address this problem: We systematize audio spoofing detection by re-implementing and uniformly evaluating architectures from related work. We identify overarching features for successful audio deepfake detection, such as using cqtspec or logspec features instead of melspec features, which improves performance by 37% EER on average, all other factors constant. Additionally, we evaluate generalization capabilities: We collect and publish a new dataset consisting of 37.9 hours of found audio recordings of celebrities and politicians, of which 17.2 hours are deepfakes. We find that related work performs poorly on such real-world data (performance degradation of up to one thousand percent). This may suggest that the community has tailored its solutions too closely to the prevailing ASVSpoof benchmark and that deepfakes are much harder to detect outside the lab than previously thought.

We demonstrate the implementation of a novel machine learning framework for probability density estimation and classification using quantum circuits. The framework maps a training data set or a single data sample to the quantum state of a physical system through quantum feature maps. The quantum state of the arbitrarily large training data set summarises its probability distribution in a finite-dimensional quantum wave function. By projecting the quantum state of a new data sample onto the quantum state of the training data set, one can derive statistics to classify or estimate the density of the new data sample. Remarkably, the implementation of our framework on a real quantum device does not require any optimisation of quantum circuit parameters. Nonetheless, we discuss a variational quantum circuit approach that could leverage quantum advantage for our framework.

The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. For instance, the personalized diagnosis and treatment planning for a single cancer patient relies on the various images (e.g., radiological, pathological, and camera images) and non-image data (e.g., clinical data and genomic data). However, such decision-making procedures can be subjective, qualitative, and have large inter-subject variabilities. With the recent advances in multi-modal deep learning technologies, an increasingly large number of efforts have been devoted to a key question: how do we extract and aggregate multi-modal information to ultimately provide more objective, quantitative computer-aided clinical decision making? This paper reviews the recent studies on dealing with such a question. Briefly, this review will include the (1) overview of current multi-modal learning workflows, (2) summarization of multi-modal fusion methods, (3) discussion of the performance, (4) applications in disease diagnosis and prognosis, and (5) challenges and future directions.

Deep learning-based Hand Gesture Recognition (HGR) via surface Electromyogram (sEMG) signals has recently shown significant potential for development of advanced myoelectric-controlled prosthesis. Existing deep learning approaches, typically, include only one model as such can hardly maintain acceptable generalization performance in changing scenarios. In this paper, we aim to address this challenge by capitalizing on the recent advances of hybrid models and transformers. In other words, we propose a hybrid framework based on the transformer architecture, which is a relatively new and revolutionizing deep learning model. The proposed hybrid architecture, referred to as the Transformer for Hand Gesture Recognition (TraHGR), consists of two parallel paths followed by a linear layer that acts as a fusion center to integrate the advantage of each module and provide robustness over different scenarios. We evaluated the proposed architecture TraHGR based on the commonly used second Ninapro dataset, referred to as the DB2. The sEMG signals in the DB2 dataset are measured in the real-life conditions from 40 healthy users, each performing 49 gestures. We have conducted extensive set of experiments to test and validate the proposed TraHGR architecture, and have compared its achievable accuracy with more than five recently proposed HGR classification algorithms over the same dataset. We have also compared the results of the proposed TraHGR architecture with each individual path and demonstrated the distinguishing power of the proposed hybrid architecture. The recognition accuracies of the proposed TraHGR architecture are 86.18%, 88.91%, 81.44%, and 93.84%, which are 2.48%, 5.12%, 8.82%, and 4.30% higher than the state-ofthe-art performance for DB2 (49 gestures), DB2-B (17 gestures), DB2-C (23 gestures), and DB2-D (9 gestures), respectively.

We introduce the dynamic grasp synthesis task: given an object with a known 6D pose and a grasp reference, our goal is to generate motions that move the object to a target 6D pose. This is challenging, because it requires reasoning about the complex articulation of the human hand and the intricate physical interaction with the object. We propose a novel method that frames this problem in the reinforcement learning framework and leverages a physics simulation, both to learn and to evaluate such dynamic interactions. A hierarchical approach decomposes the task into low-level grasping and high-level motion synthesis. It can be used to generate novel hand sequences that approach, grasp, and move an object to a desired location, while retaining human-likeness. We show that our approach leads to stable grasps and generates a wide range of motions. Furthermore, even imperfect labels can be corrected by our method to generate dynamic interaction sequences.

Current leading mispronunciation detection and diagnosis (MDD) systems achieve promising performance via end-to-end phoneme recognition. One challenge of such end-to-end solutions is the scarcity of human-annotated phonemes on natural L2 speech. In this work, we leverage unlabeled L2 speech via a pseudo-labeling (PL) procedure and extend the fine-tuning approach based on pre-trained self-supervised learning (SSL) models. Specifically, we use Wav2vec 2.0 as our SSL model, and fine-tune it using original labeled L2 speech samples plus the created pseudo-labeled L2 speech samples. Our pseudo labels are dynamic and are produced by an ensemble of the online model on-the-fly, which ensures that our model is robust to pseudo label noise. We show that fine-tuning with pseudo labels gains a 5.35% phoneme error rate reduction and 2.48% MDD F1 score improvement over a labeled-samples-only fine-tuning baseline. The proposed PL method is also shown to outperform conventional offline PL methods. Compared to the state-of-the-art MDD systems, our MDD solution achieves a more accurate and consistent phonetic error diagnosis. In addition, we conduct an open test on a separate UTD-4Accents dataset, where our system recognition outputs show a strong correlation with human perception, based on accentedness and intelligibility.

Supervised deep learning methods require a large repository of annotated data; hence, label noise is inevitable. Training with such noisy data negatively impacts the generalization performance of deep neural networks. To combat label noise, recent state-of-the-art methods employ some sort of sample selection mechanism to select a possibly clean subset of data. Next, an off-the-shelf semi-supervised learning method is used for training where rejected samples are treated as unlabeled data. Our comprehensive analysis shows that current selection methods disproportionately select samples from easy (fast learnable) classes while rejecting those from relatively harder ones. This creates class imbalance in the selected clean set and in turn, deteriorates performance under high label noise. In this work, we propose UNICON, a simple yet effective sample selection method which is robust to high label noise. To address the disproportionate selection of easy and hard samples, we introduce a Jensen-Shannon divergence based uniform selection mechanism which does not require any probabilistic modeling and hyperparameter tuning. We complement our selection method with contrastive learning to further combat the memorization of noisy labels. Extensive experimentation on multiple benchmark datasets demonstrates the effectiveness of UNICON; we obtain an 11.4% improvement over the current state-of-the-art on CIFAR100 dataset with a 90% noise rate. Our code is publicly available

The conventional wisdom behind learning deep classification models is to focus on bad-classified examples and ignore well-classified examples that are far from the decision boundary. For instance, when training with cross-entropy loss, examples with higher likelihoods (i.e., well-classified examples) contribute smaller gradients in back-propagation. However, we theoretically show that this common practice hinders representation learning, energy optimization, and margin growth. To counteract this deficiency, we propose to reward well-classified examples with additive bonuses to revive their contribution to the learning process. This counterexample theoretically addresses these three issues. We empirically support this claim by directly verifying the theoretical results or significant performance improvement with our counterexample on diverse tasks, including image classification, graph classification, and machine translation. Furthermore, this paper shows that we can deal with complex scenarios, such as imbalanced classification, OOD detection, and applications under adversarial attacks because our idea can solve these three issues. Code is available at: https://github.com/lancopku/well-classified-examples-are-underestimated.

Prior work has studied different visual modalities in isolation and developed separate architectures for recognition of images, videos, and 3D data. Instead, in this paper, we propose a single model which excels at classifying images, videos, and single-view 3D data using exactly the same model parameters. Our 'Omnivore' model leverages the flexibility of transformer-based architectures and is trained jointly on classification tasks from different modalities. Omnivore is simple to train, uses off-the-shelf standard datasets, and performs at-par or better than modality-specific models of the same size. A single Omnivore model obtains 86.0% on ImageNet, 84.1% on Kinetics, and 67.1% on SUN RGB-D. After finetuning, our models outperform prior work on a variety of vision tasks and generalize across modalities. Omnivore's shared visual representation naturally enables cross-modal recognition without access to correspondences between modalities. We hope our results motivate researchers to model visual modalities together.

Overlapping speech diarization has been traditionally treated as a multi-label classification problem. In this paper, we reformulate this task as a single-label prediction problem by encoding multiple binary labels into a single label with the power set, which represents the possible combinations of target speakers. This formulation has two benefits. First, the overlaps of target speakers are explicitly modeled. Second, threshold selection is no longer needed. Through this formulation, we propose the speaker embedding-aware neural diarization (SEND) framework, where a speech encoder, a speaker encoder, two similarity scorers, and a post-processing network are jointly optimized to predict the encoded labels according to the similarities between speech features and speaker embeddings. Experimental results show that SEND has a stable learning process and can be trained on highly overlapped data without extra initialization. More importantly, our method achieves the state-of-the-art performance in real meeting scenarios with fewer model parameters and lower computational complexity.

Object detection and forecasting are fundamental components of embodied perception. These two problems, however, are largely studied in isolation by the community. In this paper, we propose an end-to-end approach for detection and motion forecasting based on raw sensor measurement as opposed to ground truth tracks. Instead of predicting the current frame locations and forecasting forward in time, we directly predict future object locations and backcast to determine where each trajectory began. Our approach not only improves overall accuracy compared to other modular or end-to-end baselines, it also prompts us to rethink the role of explicit tracking for embodied perception. Additionally, by linking future and current locations in a many-to-one manner, our approach is able to reason about multiple futures, a capability that was previously considered difficult for end-to-end approaches. We conduct extensive experiments on the popular nuScenes dataset and demonstrate the empirical effectiveness of our approach. In addition, we investigate the appropriateness of reusing standard forecasting metrics for an end-to-end setup, and find a number of limitations which allow us to build simple baselines to game these metrics. We address this issue with a novel set of joint forecasting and detection metrics that extend the commonly used AP metrics from the detection community to measuring forecasting accuracy. Our code is available at https://github.com/neeharperi/FutureDet

The Internet of Things (IoT) boom has revolutionized almost every corner of people's daily lives: healthcare, home, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technologies, IoT devices including smart wearables, cameras, smartwatches, and autonomous vehicles can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph Neural Networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source code from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at https://github.com/GuiminDong/GNN4IoT.

Recent works in artificial intelligence fairness attempt to mitigate discrimination by proposing constrained optimization programs that achieve parity for some fairness statistic. Most assume availability of the class label, which is impractical in many real-world applications such as precision medicine, actuarial analysis and recidivism prediction. Here we consider fairness in longitudinal right-censored environments, where the time to event might be unknown, resulting in censorship of the class label and inapplicability of existing fairness studies. We devise applicable fairness measures, propose a debiasing algorithm, and provide necessary theoretical constructs to bridge fairness with and without censorship for these important and socially-sensitive tasks. Our experiments on four censored datasets confirm the utility of our approach.

Word embedding is a modern distributed word representations approach widely used in many natural language processing tasks. Converting the vocabulary in a legal document into a word embedding model facilitates subjecting legal documents to machine learning, deep learning, and other algorithms and subsequently performing the downstream tasks of natural language processing vis-\`a-vis, for instance, document classification, contract review, and machine translation. The most common and practical approach of accuracy evaluation with the word embedding model uses a benchmark set with linguistic rules or the relationship between words to perform analogy reasoning via algebraic calculation. This paper proposes establishing a 1,134 Legal Analogical Reasoning Questions Set (LARQS) from the 2,388 Chinese Codex corpus using five kinds of legal relations, which are then used to evaluate the accuracy of the Chinese word embedding model. Moreover, we discovered that legal relations might be ubiquitous in the word embedding model.

Motivated by the poor performance of cross-validation in settings where data are scarce, we propose a novel estimator of the out-of-sample performance of a policy in data-driven optimization.Our approach exploits the optimization problem's sensitivity analysis to estimate the gradient of the optimal objective value with respect to the amount of noise in the data and uses the estimated gradient to debias the policy's in-sample performance. Unlike cross-validation techniques, our approach avoids sacrificing data for a test set, utilizes all data when training and, hence, is well-suited to settings where data are scarce. We prove bounds on the bias and variance of our estimator for optimization problems with uncertain linear objectives but known, potentially non-convex, feasible regions. For more specialized optimization problems where the feasible region is "weakly-coupled" in a certain sense, we prove stronger results. Specifically, we provide explicit high-probability bounds on the error of our estimator that hold uniformly over a policy class and depends on the problem's dimension and policy class's complexity. Our bounds show that under mild conditions, the error of our estimator vanishes as the dimension of the optimization problem grows, even if the amount of available data remains small and constant. Said differently, we prove our estimator performs well in the small-data, large-scale regime. Finally, we numerically compare our proposed method to state-of-the-art approaches through a case-study on dispatching emergency medical response services using real data. Our method provides more accurate estimates of out-of-sample performance and learns better-performing policies.

Spatial clustering has been widely used for spatial data mining and knowledge discovery. An ideal multivariate spatial clustering should consider both spatial contiguity and aspatial attributes. Existing spatial clustering approaches may face challenges for discovering repeated geographic patterns with spatial contiguity maintained. In this paper, we propose a Spatial Toeplitz Inverse Covariance-Based Clustering (STICC) method that considers both attributes and spatial relationships of geographic objects for multivariate spatial clustering. A subregion is created for each geographic object serving as the basic unit when performing clustering. A Markov random field is then constructed to characterize the attribute dependencies of subregions. Using a spatial consistency strategy, nearby objects are encouraged to belong to the same cluster. To test the performance of the proposed STICC algorithm, we apply it in two use cases. The comparison results with several baseline methods show that the STICC outperforms others significantly in terms of adjusted rand index and macro-F1 score. Join count statistics is also calculated and shows that the spatial contiguity is well preserved by STICC. Such a spatial clustering method may benefit various applications in the fields of geography, remote sensing, transportation, and urban planning, etc.

If a unit cannot receive intelligence from a source due to external factors, we consider them disadvantaged users. We categorize this as a preoccupied unit working on a low connectivity device on the edge. This case requires that we use a different approach to deliver intelligence, particularly satellite imagery information, than normally employed. To address this, we propose a survey of information reduction techniques to deliver the information from a satellite image in a smaller package. We investigate four techniques to aid in the reduction of delivered information: traditional image compression, neural network image compression, object detection image cutout, and image to caption. Each of these mechanisms have their benefits and tradeoffs when considered for a disadvantaged user.

International audit standards require the direct assessment of a financial statement's underlying accounting journal entries. Driven by advances in artificial intelligence, deep-learning inspired audit techniques emerged to examine vast quantities of journal entry data. However, in regular audits, most of the proposed methods are applied to learn from a comparably stationary journal entry population, e.g., of a financial quarter or year. Ignoring situations where audit relevant distribution changes are not evident in the training data or become incrementally available over time. In contrast, in continuous auditing, deep-learning models are continually trained on a stream of recorded journal entries, e.g., of the last hour. Resulting in situations where previous knowledge interferes with new information and will be entirely overwritten. This work proposes a continual anomaly detection framework to overcome both challenges and designed to learn from a stream of journal entry data experiences. The framework is evaluated based on deliberately designed audit scenarios and two real-world datasets. Our experimental results provide initial evidence that such a learning scheme offers the ability to reduce false-positive alerts and false-negative decisions.

In classification problems, supervised machine-learning methods outperform traditional algorithms, thanks to the ability of neural networks to learn complex patterns. However, in two-class classification tasks like anomaly or fraud detection, unsupervised methods could do even better, because their prediction is not limited to previously learned types of anomalies. An intuitive approach of anomaly detection can be based on the distances from the centers of mass of the two respective classes. Autoencoders, although trained without supervision, can also detect anomalies: considering the center of mass of the normal points, reconstructions have now radii, with largest radii most likely indicating anomalous points. Of course, radii-based classification were already possible without interposing an autoencoder. In any space, radial classification can be operated, to some extent. In order to outperform it, we proceed to radial deformations of data (i.e. centric compression or expansions of axes) and autoencoder training. Any autoencoder that makes use of a data center is here baptized a centric autoencoder (cAE). A special type is the cAE trained with a uniformly compressed dataset, named the centripetal autoencoder (cpAE). The new concept is studied here in relation with a schematic artificial dataset, and the derived methods show consistent score improvements. But tested on real banking data, our radial deformation supervised algorithms alone still perform better that cAEs, as expected from most supervised methods; nonetheless, in hybrid approaches, cAEs can be combined with a radial deformation of space, improving its classification score. We expect that centric autoencoders will become irreplaceable objects in anomaly live detection based on geometry, thanks to their ability to stem naturally on geometrical algorithms and to their native capability of detecting unknown anomaly types.

The problem of resource constrained scheduling in a dynamic and heterogeneous wireless setting is considered here. In our setup, the available limited bandwidth resources are allocated in order to serve randomly arriving service demands, which in turn belong to different classes in terms of payload data requirement, delay tolerance, and importance/priority. In addition to heterogeneous traffic, another major challenge stems from random service rates due to time-varying wireless communication channels. Various approaches for scheduling and resource allocation can be used, ranging from simple greedy heuristics and constrained optimization to combinatorics. Those methods are tailored to specific network or application configuration and are usually suboptimal. To this purpose, we resort to deep reinforcement learning (DRL) and propose a distributional Deep Deterministic Policy Gradient (DDPG) algorithm combined with Deep Sets to tackle the aforementioned problem. Furthermore, we present a novel way to use a Dueling Network, which leads to further performance improvement. Our proposed algorithm is tested on both synthetic and real data, showing consistent gains against state-of-the-art conventional methods from combinatorics, optimization, and scheduling metrics.

Convolutional Neural Networks need the construction of informative features, which are determined by channel-wise and spatial-wise information at the network's layers. In this research, we focus on bringing in a novel solution that uses sophisticated optimization for enhancing both the spatial and channel components inside each layer's receptive field. Capsule Networks were used to understand the spatial association between features in the feature map. Standalone capsule networks have shown good results on comparatively simple datasets than on complex datasets as a result of the inordinate amount of feature information. Thus, to tackle this issue, we have proposed ME-CapsNet by introducing deeper convolutional layers to extract important features before passing through modules of capsule layers strategically to improve the performance of the network significantly. The deeper convolutional layer includes blocks of Squeeze-Excitation networks which use a stochastic sampling approach for progressively reducing the spatial size thereby dynamically recalibrating the channels by reconstructing their interdependencies without much loss of important feature information. Extensive experimentation was done using commonly used datasets demonstrating the efficiency of the proposed ME-CapsNet, which clearly outperforms various research works by achieving higher accuracy with minimal model complexity in complex datasets.

Despite the success of deep neural networks, there are still many challenges in deep representation learning due to the data scarcity issues such as data imbalance, unseen distribution, and domain shift. To address the above-mentioned issues, a variety of methods have been devised to explore the sample relationships in a vanilla way (i.e., from the perspectives of either the input or the loss function), failing to explore the internal structure of deep neural networks for learning with sample relationships. Inspired by this, we propose to enable deep neural networks themselves with the ability to learn the sample relationships from each mini-batch. Specifically, we introduce a batch transformer module or BatchFormer, which is then applied into the batch dimension of each mini-batch to implicitly explore sample relationships during training. By doing this, the proposed method enables the collaboration of different samples, e.g., the head-class samples can also contribute to the learning of the tail classes for long-tailed recognition. Furthermore, to mitigate the gap between training and testing, we share the classifier between with or without the BatchFormer during training, which can thus be removed during testing. We perform extensive experiments on over ten datasets and the proposed method achieves significant improvements on different data scarcity applications without any bells and whistles, including the tasks of long-tailed recognition, compositional zero-shot learning, domain generalization, and contrastive learning. Code will be made publicly available at https://github.com/zhihou7/BatchFormer.

Since facial actions such as lip movements contain significant information about speech content, it is not surprising that audio-visual speech enhancement methods are more accurate than their audio-only counterparts. Yet, state-of-the-art approaches still struggle to generate clean, realistic speech without noise artifacts and unnatural distortions in challenging acoustic environments. In this paper, we propose a novel audio-visual speech enhancement framework for high-fidelity telecommunications in AR/VR. Our approach leverages audio-visual speech cues to generate the codes of a neural speech codec, enabling efficient synthesis of clean, realistic speech from noisy signals. Given the importance of speaker-specific cues in speech, we focus on developing personalized models that work well for individual speakers. We demonstrate the efficacy of our approach on a new audio-visual speech dataset collected in an unconstrained, large vocabulary setting, as well as existing audio-visual datasets, outperforming speech enhancement baselines on both quantitative metrics and human evaluation studies. Please see the supplemental video for qualitative results at https://github.com/facebookresearch/facestar/releases/download/paper_materials/video.mp4.